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Objective : Toward the ultimate goal of cuff-less blood pressure (BP) trend tracking via pulse transit time (PTT) using wearable ballistocardiogram (BCG) signals, we present a unified approach to the gating of wearable BCG and the localization of wearable BCG waves. Methods : We present a unified approach to localize wearable BCG waves suited to various gating and localization reference signals. Our approach gates individual wearable BCG beats and identifies candidate waves in each wearable BCG beat using a fiducial point in a reference signal, and exploits a pre-specified probability distribution of the time interval between the BCG wave and the fiducial point in the reference signal to accurately localize the wave in each wearable BCG beat. We tested the validity of our approach using experimental data collected from 17 healthy volunteers. Results : We showed that our approach could localize the J wave in the wearable wrist BCG accurately with both the electrocardiogram (ECG) and the wearable wrist photoplethysmogram (PPG) signals as reference, and that the wrist BCG-PPG PTT thus derived exhibited high correlation to BP. Conclusion : We demonstrated the proof-of-concept of a unified approach to localize wearable BCG waves suited to various gating and localization reference signals compatible with wearable measurement. Significance : Prior work using the BCG itself or the ECG to gate the BCG beats and localize the waves to compute PTT are not ideally suited to the wearable BCG. Our approach may foster the development of cuff-less BP monitoring technologies based on the wearable BCG.

The objective of this study was to investigate the efficacy of pulse transit time (PTT)-pulse wave analysis (PWA) fusion in cuff-less blood pressure (BP) trend tracking based on the wearable wrist ballistocardiogram (BCG). We constructed PTT and 36 candidate BCG PWA features based on the BCG and photoplethysmogram (PPG) signals acquired at the wrist. We performed a model-based analysis to select 6 candidate BCG PWA features most sensitive to BP. We aggregated the 6 BCG PWA features into novel predictors of diastolic, systolic, and pulse BP (DP, SP, and PP) orthogonal to PTT by covariance-maximizing dimensionality reduction. Then, we evaluated and compared the efficacy of BCG PTT and BCG PTT-PWA fusion in tracking the trend of DP, SP, and PP. Unique innovations of this study, generalizable to a range of physiological signals beyond the BCG, are (i) the systematic selection of PWA features based on model-based analysis and (ii) the development of PWA-based BP predictors orthogonal to PTT. Using the experimental wrist BCG and PPG signals collected from 23 human subjects, we demonstrated that (i) BCG PTT-PWA fusion may significantly outperform BCG PTT; (ii) PTT may play the primary role in tracking BP while BCG PWA features may still have an independent and complementary value (especially in tracking PP); and (iii) model-based selection of BCG PWA features can enhance the robustness of BP trend tracking based on BCG PTT-PWA fusion.