Literature DB >> 34197317

Posture-Dependent Variability in Wrist Ballistocardiogram-Photoplethysmogram Pulse Transit Time: Implication to Cuff-Less Blood Pressure Tracking.

Sungtae Shin, Azin Mousavi, Sophia Lyle, Elisabeth Jang, Peyman Yousefian, Ramakrishna Mukkamala, Dae-Geun Jang, Ui Kun Kwon, Youn Ho Kim, Jin-Oh Hahn.   

Abstract

OBJECTIVE: Toward the ultimate goal of robust cuff-less blood pressure (BP) tracking with wrist wearables against postural changes, the goal of this work was to investigate posture-dependent variability in pulse transit time (PTT) measured with ballistocardiogram (BCG) and photoplethysmogram (PPG) signal pair at the wrist.
METHODS: BCG and PPG signals were acquired from 25 subjects under the combination of 3 body (standing, sitting, and supine) and 3 arm (vertical in head-to-foot direction, placed on the chest, and holding a shoulder) postures. PTT was computed as the time interval between the BCG J wave and the PPG foot, and the impact of the 9 postures on PTT was analyzed by invoking an array of possible physical mechanisms.
RESULTS: Our work suggests that (i) wrist BCG-PPG PTT is consistent under standing and sitting postures with vertically held arms; and (ii) changes in wrist orientation and height as well as restrictions in body and arm movement may alter wrist BCG-PPG PTT via distortions in the wrist BCG and PPG waveforms. The results indicate that wrist BCG-PPG PTT varies with respect to postures even when BP remains constant.
CONCLUSION: The potential of cuff-less BP tracking via wrist BCG-PPG PTT demonstrated under standing posture with arms vertically down in the head-to-foot direction may not generalize to other body and arm postures. SIGNIFICANCE: Understanding the physical mechanisms responsible for posture-induced BCG-PPG PTT variability may increase the versatility of the wrist BCG for cuff-less BP tracking.

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Year:  2021        PMID: 34197317     DOI: 10.1109/TBME.2021.3094200

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  1 in total

1.  Classification of Blood Volume Decompensation State via Machine Learning Analysis of Multi-Modal Wearable-Compatible Physiological Signals.

Authors:  Yekanth Ram Chalumuri; Jacob P Kimball; Azin Mousavi; Jonathan S Zia; Christopher Rolfes; Jesse D Parreira; Omer T Inan; Jin-Oh Hahn
Journal:  Sensors (Basel)       Date:  2022-02-10       Impact factor: 3.576

  1 in total

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