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

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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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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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Authors and Affiliations

  1. 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

    Yuan Xi, Zhuo Liu & Yubo Fan

  2. 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

    Shengyu Chao, Puchuan Tan & Zhou Li

  3. 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

    Sijing Cheng, Yiran Hu, Minsi Cai & Wei Hua

  4. School of Life Science, Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, 100081, China

    Yang Zou

  5. School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China

    Zhou Li

  6. Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning, 530004, China

    Zhou Li

  7. Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China

    Zhou Li

Authors
  1. Yuan Xi
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  2. Sijing Cheng
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  3. Shengyu Chao
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  4. Yiran Hu
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  5. Minsi Cai
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  6. Yang Zou
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  7. Zhuo Liu
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  8. Wei Hua
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  9. Puchuan Tan
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  10. Yubo Fan
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  11. Zhou Li
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Corresponding authors

Correspondence to Wei Hua, Puchuan Tan, Yubo Fan or Zhou Li.

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Xi, Y., Cheng, S., Chao, S. et al. Piezoelectric wearable atrial fibrillation prediction wristband enabled by machine learning and hydrogel affinity. Nano Res. 16, 11674–11681 (2023). https://doi.org/10.1007/s12274-023-5804-x

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  • DOI: https://doi.org/10.1007/s12274-023-5804-x

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