使用3D打印的原形心电图传感器对心房律乱进行AI增强诊断
Yiting Chen1, Jake Non1, Zakhar Vozovik1
1School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, V3T 0A3, Canada.
Biosensors & bioelectronics
|October 6, 2025
概括
这项研究介绍了一种可持续的,3D打印的原木心电图 (ECG) 传感器,可以干燥固定并可重复使用. 环保传感器与人工智能相结合,可实现精确的心脏监测和心律失常诊断.
科学领域:
- 生物医学工程 生物医学工程
- 材料科学 材料科学 材料科学
- 医疗保健中的人工智能
背景情况:
- 传统的银/银化物 (Ag/AgCl) 电心电图 (ECG) 电极会引起皮肤刺激,保质期有限,并产生环境废物.
- 需要可持续,可重复使用和用户友好的ECG监测解决方案.
- 目前的诊断方法可能不能促进过渡性心脏病的早期检测.
研究的目的:
- 开发一个可持续的,3D打印,原木结构的心电图传感器,作为Ag/AgCl电极的环保替代品.
- 整合一个人工智能驱动的系统,用于实时ECG分析和诊断.
- 在准确性,可重复使用性和诊断能力方面评估传感器的性能.
主要方法:
- 使用生物相容,导电性碳基墨水制造3D打印的原始结构心电图传感器.
- 传感器的机械性能 (伸展性,灵活性) 和电导率的表征.
- 开发一个人工智能系统,结合连续波形变换 (CWT) 和卷积神经网络 (CNN) 来从心电图表进行心律失常的分类.
- 对商业的Ag/AgCl电极进行ECG信号精度的验证,并评估长期监测能力 (长达34小时).
主要成果:
- 3D打印的传感器显示出高电导率 (5681 ± 122.5 S/m) 和灵活性 (2.5毫米半径的曲).
- 新型传感器获得的心电图信号的准确性与商业的Ag/AgCl电极相当.
- 人工智能系统从心电图扫描图像中实现了鼻节律的实时预诊断和十种类型的心律失常.
- 传感器被证明是可重复使用的,并且能够长时间持续监控.
结论:
- 开发的3D打印原始电图传感器为传统电极提供了一个可持续,准确和可重复使用的替代方案.
- 集成的人工智能系统为实时心脏病状况监测和诊断提供了强大的工具.
- 这一进步为远程医疗,紧急诊断和个性化健康监测带来了巨大的潜力.
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