PCA-Based Multi-Wavelength Photoplethysmography Algorithm for Cuffless Blood Pressure Measurement on Elderly Subjects.

The prevalence of hypertension has made blood pressure (BP) measurement one of the most wanted functions in wearable devices for convenient and frequent self-assessment of health conditions. The widely adopted principle for cuffless BP monitoring is based on arterial pulse transit time (PTT), which is measured with electrocardiography and photoplethysmography (PPG). To achieve cuffless BP monitoring with more compact wearable electronics, we have previously conceived a multi-wavelength PPG (MWPPG) strategy to perform BP estimation from arteriolar PTT, requiring only a single sensing node. However, challenges remain in decoding the compounded MWPPG signals consisting of both heterogeneous physiological information and motion artifact (MA). In this work, we proposed an improved MWPPG algorithm based on principal component analysis (PCA) which matches the statistical decomposition results with the arterial pulse and capillary pulse. The arteriolar PTT is calculated accordingly as the phase shift based on the entire waveforms, instead of local peak lag time, to enhance the feature robustness. Meanwhile, the PCA-derived MA component is employed to identify and exclude the MA-contaminated segments. To evaluate the new algorithm, we performed a comparative experiment (N = 22) with a cuffless MWPPG measurement device and used double-tube auscultatory BP measurement as a reference. The results demonstrate the accuracy improvement enabled by the PCA-based operations on MWPPG signals, yielding errors of 1.44 ± 6.89 mmHg for systolic blood pressure and -1.00 ± 6.71 mm Hg for diastolic blood pressure. In conclusion, the proposed PCA-based method can improve the performance of MWPPG in wearable medical devices for cuffless BP measurement.