Evidence map›Paper›PMID 42392163›Full record

ArticlePhysiological measurement2026

Dynamic beat-to-beat blood pressure estimation using a multi-modal wearable deep learning approach.

Qiao Li, Zichao Shen, Mohamed Almadi, Ye Yang, Ning Zhang, Jacqueline Gong, Gary Strangman, Ting Xiang, Yuanting Zhang, Gari D Clifford and 1 more

Abstract read
In one paragraph

Article in Physiological measurement, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Qiao LiDepartment of Biomedical Informatics, Emory University, Atlanta, GA 30322 United States of America.ORCID 0000-0002-9714-198X
Zichao ShenDepartment of Computing, Imperial College London, London, United Kingdom.ORCID 0000-0002-4809-5905
Mohamed AlmadiNeural Systems Group, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, United States of America.
Ye YangNeural Systems Group, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, United States of America.ORCID 0009-0005-5451-9646
Ning ZhangSchool of Engineering, Brown University, Providence, RI 02912, United States of America.
Jacqueline GongNeural Systems Group, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, United States of America.
Gary StrangmanNeural Systems Group, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, United States of America.
Ting XiangDepartment of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR, People's Republic of China.
Yuanting ZhangHong Kong Institutes of Medical Engineering, Hong Kong Special Administrative Region of China, People's Republic of China.
Gari D CliffordDepartment of Biomedical Informatics, Emory University, Atlanta, GA 30322 United States of America.
Quan ZhangNeural Systems Group, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, United States of America.

Funding

Wearable Multi-modality Cuffless Blood Pressure MonitoringR01EB027122 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI ZHANG, QUAN · 2021 to 2024
$3.0M
NIBIB NIH HHS R01 EB027122
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Blood PressureBlood Pressure DeterminationDeep LearningSignal Processing, Computer-AssistedWearable Electronic DevicesAdultElectrocardiographyFemaleHumansMalePhotoplethysmographyPulse Wave Analysisdeep learningdynamic blood pressure estimationmulti-modality wearable signalsnoninvasive blood pressure estimationsignal quality indexsuperficial temporal artery tonometry

Identifiers

PMID42392163
PMCPMC13402944

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.