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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.
Alonso, A.; Almuwaqqat, Z.; Chamberlain, A. Mortality in atrial fibrillation. Is it changing? Trends Cardiovasc. Med. 2021, 31, 469–473.
Salih, M.; Abdel-Hafez, O.; Ibrahim, R.; Nair, R. Atrial fibrillation in the elderly population: Challenges and management considerations. J. Arrhythmia 2021, 37, 912–921.
Song, J. S. The Chinese burden of atrial fibrillation review of atrial fibrillation studies in China. Ann. Noninvasive Electrocardiol. 2022, 27, e12957.
Wang, Z. W.; Zhang, L. F.; Chen, Z.; Wang, X.; Li, S. N.; Dong, Y.; Zheng, C. Y.; Wang, J. L.; Kang, Y. T. A2528 the disease burden of atrial fibrillation in China: Data from a national cross-section survey. J. Hypertens. 2018, 36, E281.
Bizhanov, K. A.; Abzaliyev, K. B.; Baimbetov, A. K.; Sarsenbayeva, A. B.; Lyan, E. Atrial fibrillation: Epidemiology, pathophysiology, and clinical complications (literature review). J. Cardiovasc. Electrophysiol. 2023, 34, 153–165.
Collado, F. M. S.; Von Buchwald, C. M. L.; Anderson, C. K.; Madan, N.; Suradi, H. S.; Huang, H. D.; Jneid, H.; Kavinsky, C. J. Left atrial appendage occlusion for stroke prevention in nonvalvular atrial fibrillation. J. Am. Heart Assoc. 2021, 10, e022274.
Wang, Y. C.; Xu, X. B.; Hajra, A.; Apple, S.; Kharawala, A.; Duarte, G.; Liaqat, W.; Fu, Y. W.; Li, W. J.; Chen, Y. Y. et al. Current advancement in diagnosing atrial fibrillation by utilizing wearable devices and artificial intelligence: A review study. Diagnostics 2022, 12, 689.
Gunawardene, M. A.; Willems, S. Atrial fibrillation progression and the importance of early treatment for improving clinical outcomes. Europace 2022, 24, ii22–ii28.
Tooley, J. E.; Perez, M. V. Role of digital health in detection and management of atrial fibrillation. Heart 2022, 108, 834–839.
Duncker, D.; Ding, W. Y.; Etheridge, S.; Noseworth, P. A.; Veltmann, C.; Yao, X. X.; Bunch, T. J.; Gupta, D. Smart wearables for cardiac monitoring-real-world use beyond atrial fibrillation. Sensors 2021, 21, 2539.
Sattar, Y.; Song, D.; Sarvepalli, D.; Zaidi, S. R.; Ullah, W.; Arshad, J.; Mir, T.; Zghouzi, M.; Elgendy, I. Y.; Qureshi, W. et al. Accuracy of pulsatile photoplethysmography applications or handheld devices vs. 12-lead ECG for atrial fibrillation screening: A systematic review and meta-analysis. J. Interv. Card. Electrophysiol. 2022, 65, 33–44.
Kim, H. L.; Weber, T. Pulsatile hemodynamics and coronary artery disease. Korean Circ. J. 2021, 51, 881–898.
Guo, C. X.; Jiang, Z. X.; He, H. Z.; Liao, Y. N.; Zhang, D. Wrist pulse signal acquisition and analysis for disease diagnosis: A review. Comput. Biol. Med. 2022, 143, 105312.
Chen, G. R.; Au, C.; Chen, J. Textile triboelectric nanogenerators for wearable pulse wave monitoring. Trends Biotechnol. 2021, 39, 1078–1092.
Meng, K. Y.; Xiao, X.; Wei, W. X.; Chen, G. R.; Nashalian, A.; Shen, S.; Xiao, X.; Chen, J. Wearable pressure sensors for pulse wave monitoring. Adv. Mater. 2022, 34, 2270158.
Wegner, F. K.; Plagwitz, L.; Doldi, F.; Ellermann, C.; Willy, K.; Wolfes, J.; Sandmann, S.; Varghese, J.; Eckardt, L. Machine learning in the detection and management of atrial fibrillation. Clin. Res. Cardiol. 2022, 111, 1010–1017.
Mainali, S.; Darsie, M. E.; Smetana, K. S. Machine learning in action: Stroke diagnosis and outcome prediction. Front. Neurol. 2021, 12, 734345.
Chang, C. H.; Lin, C. H.; Lane, H. Y. Machine learning and novel biomarkers for the diagnosis of Alzheimer’s disease. Int. J. Mol. Sci. 2021, 22, 2761.
Wang, Y.; Zhu, L. P.; Du, C. F. Progress in piezoelectric nanogenerators based on PVDF composite films. Micromachines 2021, 12, 1278.
Lee, C.; Park, H.; Lee, J. H. Recent structure development of poly(vinylidene fluoride)-based piezoelectric nanogenerator for self-powered sensor. Actuators 2020, 9, 57.
Tan, P. C.; Xi, Y.; Chao, S. Y.; Jiang, D. J.; Liu, Z.; Fan, Y. B.; Li, Z. An artificial intelligence-enhanced blood pressure monitor wristband based on piezoelectric nanogenerator. Biosensors 2022, 12, 234.
Cao, Y.; Yang, Y.; Qu, X. C.; Shi, B. J.; Xu, L. L.; Xue, J. T.; Wang, C.; Bai, Y.; Gai, Y. S.; Luo, D. et al. A self-powered triboelectric hybrid coder for human–machine interaction. Small Methods 2022, 6, 2101529.
Kalimuldina, G.; Turdakyn, N.; Abay, I.; Medeubayev, A.; Nurpeissova, A.; Adair, D.; Bakenov, Z. A review of piezoelectric PVDF film by electrospinning and its applications. Sensors 2020, 20, 5214.
Mirjalali, S.; Varposhti, A. M.; Abrishami, S.; Bagherzadeh, R.; Asadnia, M.; Huang, S. J.; Peng, S. H.; Wang, C. H.; Wu, S. Y. A review on wearable electrospun polymeric piezoelectric sensors and energy harvesters. Macromol. Mater. Eng. 2023, 308, 2200442.
Arica, T. A.; Isık, T.; Guner, T.; Horzum, N.; Demir, M. M. Advances in electrospun fiber-based flexible nanogenerators for wearable applications. Macromol. Mater. Eng. 2021, 306, 2100143.
Gai, Y. S.; Wang, E. G.; Liu, M. H.; Xie, L. R.; Bai, Y.; Yang, Y.; Xue, J. T.; Qu, X. C.; Xi, Y.; Li, L. L. et al. A self-powered wearable sensor for continuous wireless sweat monitoring. Small Methods 2022, 6, 2200653.
Zhang, J.; Wang, Y. E.; Wei, Q. H.; Wang, Y. M.; Lei, M. J.; Li, M. Y.; Li, D. H.; Zhang, L. Y.; Wu, Y. Self-healing mechanism and conductivity of the hydrogel flexible sensors: A review. Gels 2021, 7, 216.
Chao, S. Y.; Ouyang, H.; Jiang, D. J.; Fan, Y. B.; Li, Z. Triboelectric nanogenerator based on degradable materials. EcoMat 2021, 3, e12072.
Wang, C.; Liu, Y.; Qu, X. C.; Shi, B. J.; Zheng, Q.; Lin, X. B.; Chao, S. Y.; Wang, C. Y.; Zhou, J.; Sun, Y. et al. Ultra-stretchable and fast self-healing ionic hydrogel in cryogenic environments for artificial nerve fiber. Adv. Mater. 2022, 34, 2105416.
Liu, Y.; Wang, C.; Xue, J. T.; Huang, G. H.; Zheng, S.; Zhao, K.; Huang, J.; Wang, Y. Q.; Zhang, Y.; Yin, T. L. et al. Body temperature enhanced adhesive, antibacterial, and recyclable ionic hydrogel for epidermal electrophysiological monitoring. Adv. Health. Mater. 2022, 11, 2270092.
Xu, J. P.; Tsai, Y. L.; Hsu, S. H. Design strategies of conductive hydrogel for biomedical applications. Molecules 2020, 25, 5296.
Nele, V.; Wojciechowski, J. P.; Armstrong, J. P. K.; Stevens, M. M. Tailoring gelation mechanisms for advanced hydrogel applications. Adv. Funct. Mater. 2020, 30, 2002759.
Mynard, J. P.; Kondiboyina, A.; Kowalski, R.; Cheung, M. M. H.; Smolich, J. J. Measurement, analysis, and interpretation of pressure/flow waves in blood vessels. Front. Physiol. 2020, 11, 1085.