Utilizing Multi-PPG-Sensor Site Information in a Localized Wrist Area for Improving Cuffless Blood Pressure Estimation

Blood pressure (BP) measurement accuracy is highly sensitive to sensor placement. To address this, we investigated the effect of multi-sensor sites on BP estimation using a 9-channel single-wavelength photoplethysmography (PPG) sensor array placed within a localized wrist area. As a starting point, we analyzed the variability of 9 PPG features across channels, revealing notable site-specific variations, with amplitude-based features showing greater sensitivity. Leveraging these findings, we developed 3M-BPNet, which processes PPG signals through varying channel/site counts. The network incorporates signal trimming and Bayesian optimization for channel-weight allocation, followed by a Random Forest Regression (RFR) model with personalized calibration. Tested on 121 subjects, the 3M-BPNet achieved mean absolute errors (MAEs) of 4.84 mmHg for systolic BP (SBP) and 3.28 mmHg for diastolic BP (DBP), outperforming a standard RFR model. Notably, BP estimation accuracy improved as the channel counts increased from 1 to 5, then declined beyond 5. The optimal 5-channel combination (C5- C6-C7-C8-C9), located near the radial artery, yielded MAEs of 1.33 mmHg for SBP and 1.16 mmHg for DBP, corresponding to accuracy gains of 72.6% for SBP and 73.2% for DBP over the single-channel setup (P < 0.001 for both MAE_SBP and MAE_DBP). Compared with the best prior PPG-based BP estimation results, our method reduced SBP MAE by 71.9% and DBP MAE by 51.3%. These findings highlight the critical role of sensor location in PPG-based BP estimation, suggesting that optimized sensor placement can enhance the accuracy and guide the design of wearable cuffless BP devices, thereby advancing hypertension management.