Evidence map›Paper›PMID 36582736›Full record

ArticleFrontiers in cardiovascular medicine2022

Contactless multiscale measurement of cardiac motion using biomedical radar sensor.

Jia-Hao Qiao, Fu-Gui Qi, Fu-Lai Liang, Jin Ma, Hao Lv, Xiao Yu, Hui-Jun Xue, Qiang An, Ke-Ding Yan, Ding Shi and 3 more

Abstract read
In one paragraph

Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. [Microwave Heartprint: A novel non-contact human identification technology based on cardiac micro-motion detection using ultra wideband bio-radar].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024
    Article
  6. Article
  7. A Review and Tutorial on Machine Learning-Enabled Radar-Based Biomedical Monitoring.IEEE open journal of engineering in medicine and biology · 2024
    Article
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

13 authors.

Jia-Hao QiaoDepartment of Military Biomedical Engineering, Fourth Military Medical University, Xi'an, China.
Fu-Gui QiDepartment of Military Biomedical Engineering, Fourth Military Medical University, Xi'an, China.
Fu-Lai LiangDepartment of Military Biomedical Engineering, Fourth Military Medical University, Xi'an, China.
Jin MaSchool of Aerospace Medicine, Fourth Military Medical University, Xi'an, China.
Hao LvDepartment of Military Biomedical Engineering, Fourth Military Medical University, Xi'an, China.
Xiao YuDepartment of Military Biomedical Engineering, Fourth Military Medical University, Xi'an, China.
Hui-Jun XueDepartment of Military Biomedical Engineering, Fourth Military Medical University, Xi'an, China.
Qiang AnDepartment of Military Biomedical Engineering, Fourth Military Medical University, Xi'an, China.
Ke-Ding YanSchool of Electronic Information Engineering, Xi'an Technological University, Xi'an, China.
Ding ShiDepartment of Military Biomedical Engineering, Fourth Military Medical University, Xi'an, China.
Yong-Hui QiaoState Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, China.
Jian-Qi WangDepartment of Military Biomedical Engineering, Fourth Military Medical University, Xi'an, China.
Yang ZhangDepartment of Military Biomedical Engineering, Fourth Military Medical University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: A contactless multiscale cardiac motion measurement method is proposed using impulse radio ultra-wideband (IR-UWB) radar at a center frequency of 7.29 GHz. Motivation: Electrocardiograph (ECG), heart sound, and ultrasound are traditional state-of-the-art heartbeat signal measurement methods. These methods suffer from defects in contact and the existence of a blind information segment during the cardiogram measurement. Methods: Experiments and analyses were conducted using coarse-to-fine scale. Anteroposterior and along-the-arc measurements were taken from five healthy male subjects (aged 25-43) when lying down or prone. In every measurement, 10 seconds of breath-holding data were recorded with a radar 55 cm away from the body surface, while the ECG was monitored simultaneously as a reference. Results: Cardiac motion detection from the front was superior to that from the back in amplitude. In terms of radar detection angles, the best cardiac motion information was observed at a detection angle of 120°. Finally, in terms of cardiac motion cycles, all the ECG information, as well as short segments of cardiac motion details named blind ECGs segments, were detected. Significance: A contactless and multiscale cardiac motion detection method is proposed with no blind detection of segments during the entire cardiac cycle. This paves the way for a potentially significant method of fast and accurate cardiac disease assessment and diagnosis that exhibits promising application prospects in contactless online cardiac monitoring and in-home healthcare.

Indexed as

along-the-arc measurementsanteroposterior measurementscardiac motiondiastasis measurementradar cardiogram (RCG)

Identifiers

PMID36582736
PMCPMC9792510

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Registered trials

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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.