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Nano Research 2023, 16(9): 11674-11681 https://doi.org/10.1007/s12274-023-5804-x
Research Article | Issue | Published: 31 May 2023

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

Show Author's Information Yuan Xi1,  Sijing Cheng3,  Shengyu Chao2,  Yiran Hu3,  Minsi Cai3,  Yang Zou4,  Zhuo Liu1,  Wei 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
Keywords:
machine learning, piezoelectricity, wristband, atrial fibrillation prediction
Topics:
Biotechnology
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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Abstract Full text Electronic supplementary material About this article

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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Abstract
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Electronic supplementary material
About this article

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

Show Author's information Yuan Xi1, Sijing Cheng3, Shengyu Chao2, Yiran Hu3, Minsi Cai3, Yang Zou4, Zhuo Liu1, Wei 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

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.

Keywords: machine learning, piezoelectricity, wristband, atrial fibrillation prediction

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Publication history
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Acknowledgements

Publication history

Received: 01 February 2023
Revised: 07 April 2023
Accepted: 03 May 2023
Published: 31 May 2023
Issue date: September 2023

Copyright

© Tsinghua University Press 2023

Acknowledgements

Acknowledgements

This study was supported by the National Natural Science Foundation of China (Nos. T2125003, 82202075, and 82102231), the Beijing Natural Science Foundation (Nos. JQ20038 and L212010), the National Postdoctoral Program for Innovative Talent (No. BX20220380), and the China Postdoctoral Science Foundation (No. 2022M710389). The authors thank everyone who contributed to this work.

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