Fully organic compliant dry electrodes self-adhesive to skin for long-term motion-robust epidermal biopotential monitoring

Lei Zhang, Kirthika Senthil Kumar, Hao He, Catherine Jiayi Cai, Xu He, Huxin Gao, Shizhong Yue, Changsheng Li, Raymond Chee-Seong Seet, Hongliang Ren () and Jianyong Ouyang ()
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Lei Zhang: National University of Singapore, Faculty of gineering
Kirthika Senthil Kumar: National University of Singapore, Faculty of Engineering
Hao He: National University of Singapore, Faculty of gineering
Catherine Jiayi Cai: National University of Singapore, Faculty of Engineering
Xu He: National University of Singapore, Faculty of gineering
Huxin Gao: National University of Singapore, Faculty of Engineering
Shizhong Yue: National University of Singapore, Faculty of gineering
Changsheng Li: National University of Singapore, Faculty of Engineering
Raymond Chee-Seong Seet: National University Health System
Hongliang Ren: National University of Singapore, Faculty of Engineering
Jianyong Ouyang: National University of Singapore, Faculty of gineering

Nature Communications, 2020, vol. 11, issue 1, 1-13

Abstract: Abstract Wearable dry electrodes are needed for long-term biopotential recordings but are limited by their imperfect compliance with the skin, especially during body movements and sweat secretions, resulting in high interfacial impedance and motion artifacts. Herein, we report an intrinsically conductive polymer dry electrode with excellent self-adhesiveness, stretchability, and conductivity. It shows much lower skin-contact impedance and noise in static and dynamic measurement than the current dry electrodes and standard gel electrodes, enabling to acquire high-quality electrocardiogram (ECG), electromyogram (EMG) and electroencephalogram (EEG) signals in various conditions such as dry and wet skin and during body movement. Hence, this dry electrode can be used for long-term healthcare monitoring in complex daily conditions. We further investigated the capabilities of this electrode in a clinical setting and realized its ability to detect the arrhythmia features of atrial fibrillation accurately, and quantify muscle activity during deep tendon reflex testing and contraction against resistance.

Date: 2020
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DOI: 10.1038/s41467-020-18503-8

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