Abstract

This study introduces a flexible, adhesive-integrated electrode array that was developed to enable non-invasive monitoring of cervical nerve activity. The device uses silver-silver chloride as the electrode material of choice and combines it with an electrode array consisting of a customized biopotential data acquisition unit and integrated graphical user interface (GUI) for visualization of real-time monitoring. Preliminary testing demonstrated this electrode design can achieve a high signal to noise ratio during cervical neural recordings. To demonstrate the capability of the surface electrodes to detect changes in cervical neuronal activity, the cold-pressor test (CPT) and a timed respiratory challenge were employed as stressors to the autonomic nervous system. This sensor system recording, a new technique, was termed Cervical Electroneurography (CEN). By applying a custom spike sorting algorithm to the electrode measurements, neural activity was classified in two ways: (1) pre-to-post CPT, and (2) during a timed respiratory challenge. Unique to this work: (1) rostral to caudal channel position-specific (cephalad to caudal) firing patterns and (2) cross challenge biotype-specific change in average CEN firing, were observed with both CPT and the timed respiratory challenge. Future work is planned to develop an ambulatory CEN recording device that could provide immediate notification of autonomic nervous system activity changes that might indicate autonomic dysregulation in healthy subjects and clinical disease states.

Details

Title
A flexible adhesive surface electrode array capable of cervical electroneurography during a sequential autonomic stress challenge
Author
Bu, Yifeng 1 ; Kurniawan, Jonas F. 2 ; Prince, Jacob 1 ; Nguyen, Andrew K. L. 3 ; Ho, Brandon 1 ; Sit, Nathan L. J. 1 ; Pham, Timothy 4 ; Wu, Vincent M. 5 ; Tjhia, Boris 4 ; Shin, Andrew J. 6 ; Wu, Tsung-Chin 7 ; Tu, Xin M. 7 ; Rao, Ramesh 1 ; Coleman, Todd P. 5 ; Lerman, Imanuel 8 

 University of California San Diego, Department of Electrical and Computer Engineering, La Jolla, USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) 
 University of California San Diego, Materials Science and Engineering Program, La Jolla, USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) 
 University of California San Diego, Department of Physics, La Jolla, USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) 
 University of California San Diego, Department of Nanoengineering, La Jolla, USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) 
 University of California San Diego, Department of Bioengineering, La Jolla, USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) 
 Stanford University, Department of Materials Science and Engineering, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956) 
 University of California San Diego, Division of Biostatistics and Bioinformatics, La Jolla, USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) 
 University of California San Diego, Department of Electrical and Computer Engineering, La Jolla, USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242); University of California San Diego, Department of Anesthesiology, La Jolla, USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242); VA San Diego, Department of Psychiatry, Center for Stress and Mental Health, La Jolla, USA (GRID:grid.266100.3) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2736092835
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.