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Fully integrated flexible long-term electrocardiogram recording patch with gel-less adhesive electrodes for arrhythmia detection

微控制器 电极 材料科学 计算机科学 噪音(视频) 信号(编程语言) 聚二甲基硅氧烷 可穿戴计算机 块(置换群论) 计算机硬件 电子工程 人工智能 嵌入式系统 工程类 纳米技术 物理化学 化学 图像(数学) 程序设计语言 数学 几何学
作者
Li Dong,Bing Zhang,Youze Xin,Yanze Liu,Rui Xing,Yawei Yang,Xiaofei Wang,Wenxiu Que,Weihua Liu,Li Geng
出处
期刊:Sensors and Actuators A-physical [Elsevier]
卷期号:332: 113063-113063 被引量:13
标识
DOI:10.1016/j.sna.2021.113063
摘要

A mechanically flexible and fully integrated electrocardiogram (ECG) recording patch (ERP) that simultaneously monitors the ECG signals and detects the arrhythmia is presented. A simplified two-step process is applied to fabricate the acetylene carbon black (AB)/ polydimethylsiloxane (PDMS) electrodes. The soft and adhesive nature of polymer composite electrodes allows conformal contact to the skin. The ERP integrates ECG front end circuits, microcontroller chip and wireless communication unit on a flexible printed circuit boards (FPCB) with electrodes attached on its back, which bridges the technological gap among the signal acquisition, processing, and wireless transmission in the wearable electronics. The ERP can be deformed easily under stretching and bending actions by a special connection of two squares through serpentine traces. With the concentration of 7 wt% AB, the average signal-to-noise ratio (SNR) of the built electrodes at different postures (sit, stand and walk) during electrocardiography are assessed as 17.4 dB, 16.1 dB and 5.6 dB, respectively. Similarly, the SNR of the ERP with Ag/AgCl electrodes are 22.1 dB, 20.6 dB and 10.3 dB, respectively. The performance of the AB/PDMS electrodes are very close to the standard Ag/AgCl electrodes. In addition, the ERP have the ability of anti-interference from environmental noise and motion artifacts (MA). Benefited from implemented microcontroller unit (MCU) platform, the ERP allows sophisticated signal processing with the introduction of discrete wavelet transform (DWT) and support vector machine (SVM) algorithms, which achieves high classification accuracy of 98.7% for online arrhythmia detection. The designed platform provides a wide range of applications for other personalized physiological monitoring and diagnosis.
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