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Research Article

Piezoelectric wearable atrial fibrillation prediction wristband enabled by machine learning and hydrogel affinity

Yuan Xi1Sijing Cheng3Shengyu Chao2Yiran Hu3Minsi Cai3Yang Zou4Zhuo Liu1Wei Hua3( )Puchuan Tan2( )Yubo Fan1( )Zhou Li2,5,6,7( )
Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, School of Engineering Medicine, Beihang University, Beijing 100191, China
CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-nano Energy and Sensor Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
The Cardiac Arrhythmia Center, State Key Laboratory of Cardiovascular Disease, National Clinical Research Center of Cardiovascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
School of Life Science, Institute of Engineering Medicine, Beijing Institute of Technology, Beijing 100081, China
School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning 530004, China
Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
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Graphical Abstract

Wearable atrial fibrillation prediction wristband (AFPW) can provide long-term monitoring and atrial fibrillation diagnosis. AFPW has a hydrogel skin affinity interface and uses machine learning algorithms to provide atrial fibrillation prediction.

Abstract

Atrial fibrillation (AF) is a common and serious disease. Its diagnosis usually requires 12-lead electrocardiogram, which is heavy and inconvenient. At the same time, the venue for diagnosis is also limited to the hospital. With the development of the concept of intelligent medical, a wearable, portable, and reliable diagnostic method is needed to improve the patient’s comfort and alleviate the patient’s pain. Here, we reported a wearable atrial fibrillation prediction wristband (AFPW) which can provide long-term monitoring and AF diagnosis. AFPW uses polyvinylidene fluoride piezoelectric film as sensing material and hydrogel as skin bonding material, of which the structure and design have been optimized and improved. The hydrogel skin bonding layer has good stability and skin affinity, which can greatly improve the user experience. AFPW has enhanced signal, strong signal-to-noise ratio, and wireless transmission function. After a sample library of 385 normal people/patients is analyzed and tested by linear discriminant analysis, the diagnostic success rate of atrial fibrillation is 91%. All these excellent performances demonstrate the great application potential of AFPW in wearable device diagnosis and intelligent medical treatment.

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Nano Research
Pages 11674-11681
Cite this article:
Xi Y, Cheng S, Chao S, et al. Piezoelectric wearable atrial fibrillation prediction wristband enabled by machine learning and hydrogel affinity. Nano Research, 2023, 16(9): 11674-11681. https://doi.org/10.1007/s12274-023-5804-x
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Received: 01 February 2023
Revised: 07 April 2023
Accepted: 03 May 2023
Published: 31 May 2023
© Tsinghua University Press 2023
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