Pulse Transit Time-Pulse Wave Analysis Fusion Based on Wearable Wrist Ballistocardiogram for Cuff-Less Blood Pressure Trend Tracking

• Published in: IEEE access Vol. 8; p. 1
• Main Authors: Yousefian, Peyman, Shin, Sungtae, Mousavi, Azin, Tivay, Ali, Kim, Chang-Sei, Mukkamala, Ramakrishna, Jang, Dae-Geun, Ko, Byung-Hoon, Lee, Jongwook, Kwon, Ui-Kun, Kim, Youn Ho, Hahn, Jin-Oh
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Ballistocardiogram; Ballistocardiograms; Biomedical monitoring; Blood pressure; Covariance; cuff-less blood pressure monitoring; Mathematical model; mobile health; Monitoring; photoplethysmogram; pulse transit time; pulse wave analysis; Tracking; Transit time; wearable health; Wearable technology; Wrist
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3012384

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.