A Finger on the Pulse of Cardiovascular Health: Smartphone Photoplethysmography-Based Pulse Waveform Analysis for Blood Pressure Measurement

Routine blood pressure (BP) monitoring, crucial for health assessment, faces challenges such as limited access to medical-grade equipment and expertise. Portable cuff BP devices, on the other hand, are cumbersome to carry all day and often cost-prohibitive in less developed countries. Besides, these sphygmomanometer-based devices can cause discomfort and disrupt blood flow during measurement. This study explores the use of smartphones for continuous BP monitoring, focusing on overcoming the trust barriers associated with the opacity of machine learning models in predicting BP from low-quality PPG signals. Our approach included developing models based on cardiovascular literature, using simple statistical methods to estimate BP from smartphone PPG signals with comprehensive data pre-processing, applying SHAP for enhanced interpretability and feature identification, and comparing our methods against standard references using Bland-Altman analysis. Validated with data from 125 participants, the study demonstrated significant correlations in waveform features between smartphone and reference BP monitoring devices. The cross-validation of linear regression [MAE=9.86 and 8.01 mmHg for systolic blood pressure (SBP) and diastolic blood pressure (DBP), respectively] and random forest model (MAE=8.91 and 6.68 mmHg for SBP and DBP) using waveform-only variables demonstrated the feasibility of using a smartphone to estimate BP. Although SHAP analysis identified key feature sets, Bland-Altman results did not fully meet established thresholds (84.64% and 94.69% of MAE<15 mmHg for SBP and DBP, respectively). The study suggests the potential of smartphone cameras to enhance the accuracy and interpretability of machine learning models for daily BP estimation, but also indicates that smartphone PPG-based BP prediction is not yet a replacement for traditional medical devices.