Evidence map›Paper›PMID 39638816›Full record

ArticleNature communications2024

Monitoring long-term cardiac activity with contactless radio frequency signals.

Bin-Bin Zhang, Dongheng Zhang, Yadong Li, Zhi Lu, Jinbo Chen, Haoyu Wang, Fang Zhou, Yu Pu, Yang Hu, Li-Kun Ma and 2 more

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Discovering Interpretable Semantics from Radio Signals for Contactless Cardiac Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Antifouling All-Polymeric Microneedle Array for Long-Term Wearable ECG Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  8. Review
  9. 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

12 authors.

Bin-Bin ZhangSchool of Cyber Science and Technology, University of Science and Technology of China, Hefei, China.ORCID 0000-0002-7164-5127
Dongheng ZhangSchool of Cyber Science and Technology, University of Science and Technology of China, Hefei, China.
Yadong LiDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.ORCID 0009-0004-3654-965X
Zhi LuSchool of Cyber Science and Technology, University of Science and Technology of China, Hefei, China.ORCID 0000-0001-6941-981X
Jinbo ChenSchool of Cyber Science and Technology, University of Science and Technology of China, Hefei, China.ORCID 0000-0003-4532-3236
Haoyu WangSchool of Cyber Science and Technology, University of Science and Technology of China, Hefei, China.ORCID 0009-0004-5365-9674
Fang ZhouSchool of Cyber Science and Technology, University of Science and Technology of China, Hefei, China.
Yu PuSchool of Cyber Science and Technology, University of Science and Technology of China, Hefei, China.
Yang HuSchool of Cyber Science and Technology, University of Science and Technology of China, Hefei, China.
Li-Kun MaThe First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China.ORCID 0000-0002-9680-1472
Qibin SunSchool of Cyber Science and Technology, University of Science and Technology of China, Hefei, China.
Yan ChenSchool of Cyber Science and Technology, University of Science and Technology of China, Hefei, China. eecyan@ustc.edu.cn.ORCID 0000-0002-3227-4562

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiovascular diseases claim over 10 million lives annually, highlighting the critical need for long-term monitoring and early detection of cardiac abnormalities. Existing techniques like electrocardiograms (ECG) and Holter are accurate but suffer from discomfort caused by body-attached electrodes. While wearable devices using photoplethysmography offer more convenience, they sacrifice accuracy and are susceptible to environmental interference. Here we present a radio frequency (RF)-based (60 to 64 GHz) sensing system that monitors long-term heart rate variability (HRV) with clinical-grade accuracy. Our system successfully overcomes the orders-larger interference from respiration motion in far-field conditions without any model training. By identifying previously undiscovered frequency ranges (beyond 10-order heartbeat harmonics) where heartbeat information predominates over other motions, we generate prominent heartbeat patterns with harmonics typically considered detrimental. Extensive evaluations, including a large-scale outpatient setting involving 6,222 eligible participants and a long-term daily life scenario, where sleep data was collected over 5 separate random nights over two months and a continuous 21-night period, demonstrate that our system can monitor HRV and identify abnormalities with comparable performance to clinical-grade ECG-based systems. This RF-based HRV sensing system has the potential to support active self-assessment and revolutionize medical prevention with long-term and precise health monitoring.

Indexed as

Heart RateRadio WavesAdolescentAdultAgedCardiovascular DiseasesElectrocardiographyFemaleHumansMaleMiddle AgedMonitoring, PhysiologicSleepWearable Electronic DevicesYoung Adult

Identifiers

PMID39638816
PMCPMC11621424

What OpenQuestion holds

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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.