Mapping of spatiotemporal auricular electrophysiological signals reveals human biometric clusters

Huang, Q. et al. (2022) Mapping of spatiotemporal auricular electrophysiological signals reveals human biometric clusters. Advanced Healthcare Materials, 11(23), 2201404. (doi: 10.1002/adhm.202201404) (PMID:36217916)

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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.

Item Type:Articles
Additional Information:The authors thank Mr. M. Chen and Mr. Y. Lu for providing electrical circuit design advice, and Mr. L. Gao and Ms. X. Ren for their assistance with human experiments. This work was supported by the Hong Kong Research Grants Council’s Joint Laboratory Funding Scheme (Grants No. JLFS/E104/18), the Hong Kong Research Grants Council’s General Research Fund (Grant No. 11204918), the University of Hong Kong Seed Fund for Translational and Applied Research (Grant No. 201711160034), the Hong Kong Centre for Cerebro-cardiovascular Health Engineering (COCHE), Lingang Laboratory (Grant No. LG-QS-202202-02), the Shanghai Municipal Science and Technology Major Project (Grant No. 2018SHZDZX01), ZJ Lab, and Shanghai Center for Brain Science and Brain-Inspired Technology.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Vellaisamy, Professor Roy
Authors: Huang, Q., Wu, C., Hou, S., Yao, K., Sun, H., Wang, Y., Chen, Y., Law, J., Yang, M., Chan, H.‐y., Roy, V. A. L., Zhao, Y., Wang, D., Song, E., Yu, X., Lao, L., Sun, Y., and Li, W. J.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Journal Name:Advanced Healthcare Materials
Publisher:Wiley
ISSN:2192-2640
ISSN (Online):2192-2659
Published Online:11 October 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Advanced Healthcare Materials 11(23): 2201404
Publisher Policy:Reproduced under a Creative Commons License

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