Evidence map›Paper›PMID 36217916›Full record

ArticleAdvanced healthcare materials2022

Mapping of Spatiotemporal Auricular Electrophysiological Signals Reveals Human Biometric Clusters.

Qingyun Huang, Cong Wu, Senlin Hou, Kuanming Yao, Hui Sun, Yufan Wang, Yikai Chen, Junhui Law, Mingxiao Yang, Ho-Yin Chan and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced healthcare materials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

18 authors.

Qingyun HuangDepartment of Mechanical Engineering, City University of Hong Kong, Hong Kong, 999077, P. R. China.ORCID 0000-0001-8738-4382
Cong WuDepartment of Mechanical Engineering, City University of Hong Kong, Hong Kong, 999077, P. R. China.
Senlin HouDepartment of Mechanical Engineering, City University of Hong Kong, Hong Kong, 999077, P. R. China.
Kuanming YaoDepartment of Biomedical Engineering, City University of Hong Kong, Hong Kong, 999077, P. R. China.
Hui SunDepartment of Mechanical Engineering, City University of Hong Kong, Hong Kong, 999077, P. R. China.
Yufan WangDepartment of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China.
Yikai ChenDepartment of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China.
Junhui LawDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, M5S 3G8, Canada.
Mingxiao YangBendheim Integrative Medicine Center, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
Ho-Yin ChanDepartment of Mechanical Engineering, City University of Hong Kong, Hong Kong, 999077, P. R. China.
Vellaisamy A L RoyJames Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK.
Yuliang ZhaoSchool of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, 066004, P. R. China.
Dong WangDepartment of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China.
Enming SongShanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception, Institute of Optoelectronics, Fudan University, Shanghai, 200438, P. R. China.
Xinge YuHong Kong Centre for Cerebro-cardiovascular Health Engineering (COCHE), Hong Kong Science Park, New Territories, Hong Kong, 999077, P. R. China.
Lixing LaoVirginia University of Integrative Medicine, Vienna, VA, 22182, USA.
Yu SunDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, M5S 3G8, Canada.
Wen Jung LiDepartment of Mechanical Engineering, City University of Hong Kong, Hong Kong, 999077, P. R. China.ORCID 0000-0001-9616-6213

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Underneath the ear skin there are rich vascular network and sensory nerve branches. Hence, the 3D mapping of auricular electrophysiological signals can provide new biomedical perspectives. However, it is still extremely challenging for current sensing techniques to cover the entire ultra-curved auricle. Here, a 3D graphene-based ear-conformable sensing device with embedded and distributed 3D electrodes for full-auricle physiological monitoring is reported. As a proof-of-concept, spatiotemporal auricular electrical skin resistance (AESR) mapping is demonstrated for the first time, and human subject-specific AESR distributions are observed. From the data of more than 30 ears (both right and left ears), the auricular region-specific AESR changes after cycling exercise are observed in 98% of the tests and are clustered into four groups via machine learning-based data analyses. Correlations of AESR with heart rate and blood pressure are also studied. This 3D electronic platform and AESR-based biometrical findings show promising biomedical applications.

Indexed as

ElectronicsHumansfull-auricle electrophysiological monitoringgraphene-based 3D electrodeshuman biometric clustersmachine learningpersonalized healthcare sensors

Identifiers

PMID36217916
PMCPMC11469291

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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.