通过使用卷积神经网络和基于复发的图表从脉冲波检测心房动
Hiroyuki Kitajima1, Kentaro Takeda1, Makoto Ishizawa2
1Faculty of Engineering and Design, Kagawa University, 2217-20, Hayashi, Takamatsu, Kagawa 761-0396, Japan.
Chaos (Woodbury, N.Y.)
|March 14, 2025
概括
我们开发了一种新方法,使用血压监测器从脉冲波中检测心房动 (AFib). 这种技术实现了高精度,超过了以前的血压监测方法.
科学领域:
- 生物医学工程 生物医学工程
- 心血管技术的心血管技术
- 医疗保健中的人工智能
背景情况:
- 心房动 (AFib) 的检测通常依赖于心电图 (ECG) 或光电脉图 (PPG).
- 使用血压监测器 (BPM) 进行AFib分类的现有方法在准确性和灵敏性方面存在局限性.
研究的目的:
- 开发和验证一种新型分类方法,用于区分心房动与鼻节律,使用标准血压计的脉冲波信号.
- 为了提高AFib检测的诊断准确性,非侵入性地使用随时可用的设备.
主要方法:
- 开发了一种新的分类方法,将基于复发的图形与卷积神经网络 (CNN) 结合起来.
- 脉冲波信号是使用常规血压计获取的.
- 设计了一个独特的重复情节可视化来增强特征提取.
主要成果:
- 拟议的方法实现了高诊断性能:97.5%的特异性,98.4%的灵敏性和98.6%的准确性.
- 这些结果超过了BPM-based AFib检测之前报告的指标.
- 性能与已建立的基于ECG和PPG的方法密切相匹敌.
结论:
- 开发的方法提供了一个有希望的,准确的,可访问的方法,用于使用血压监测器查心房动.
- 这一创新可以促进在各种临床和家庭环境中广泛的非侵入性AFib检测.
- 进一步的验证可能将BPM确立为心血管节律监测的可行工具.
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