Piezoelectric wearable atrial fibrillation prediction wristband enabled by machine learning and hydrogel affinity
Xi Yuan, Sijing Cheng, Shengyu Chao, Yiran Hu, Minsi Cai, Yang Zou, Zhuo Liu, Wei Hua, Puchuan Tan, Yubo Fan, Zhou Li
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.