@article{Li2025, 
author = {Deliang Li and Hongxing Zhou and Qingxin Tang and Guanshi Liu and Huilin Yuan and Lexu Wu and Hongguo Wei and Jinhong Du and Kexin Fu and He Liu and Yumo She and Wensha Chen and Haoqi Bai and Lei Ouyang and Ying Liu and Tianhang Nan and Xiaoyu Cui and Ye Tian},
title = {Dual-bridge ionic-electronic amphoteric hydrogel based e-skin for 12-lead ECG monitoring},
year = {2025},
journal = {Nano Research},
volume = {18},
number = {9},
pages = {94907616},
keywords = {emotion recognition, electronic skin, conductive hydrogel, electrocardiogram monitoring},
url = {https://www.sciopen.com/article/10.26599/NR.2025.94907616},
doi = {10.26599/NR.2025.94907616},
abstract = {Electrocardiogram (ECG) monitoring is crucial for cardiovascular health assessment, yet often encumbered by bulky equipment, limited mobility, and professional application requirements of traditional ECG monitoring systems. Moreover, hydrogel-based bioelectrodes are facing challenges of high impedance and poor overall circuit flexibility. Addressing these multifaceted challenges, this study introduces a revolutionary approach through the development of a flexible electronic skin (e-skin) incorporating a novel dual-bridge ionic-electronic amphoteric (DBIEA) hydrogel. Our proposed dual-bridge strategy for synthesizing DBIEA hydrogel yields materials with excellent electrical conductivity (~ 5000 S/m) and unique ion-electron amphoteric properties. When applied as bioelectrodes, these hydrogels demonstrate tremendous potential in biological signal monitoring. Utilizing these DBIEA hydrogel, we develop an all-stretchable e-skin (ECG-Skin) for continuous 12-lead ECG monitoring. This ECG-Skin is characterized by its ultra-lightweight design, with a total mass not exceeding 20 g and a thickness of less than 1 mm. It exhibits superior signal quality, simplifies the ECG monitoring process by eliminating the need for medical professionals, and offers exceptional wearability. In the emotion recognition task utilizing ECG-Skin for monitoring 12-lead ECG, the convolutional neural networks (CNN)-based classification achieves an accuracy rate of 98.429%. These features collectively provide unprecedented portability in ECG monitoring technology.}
}