基于光电脉学检测心房动:一个不断增长的领域
Cheng Ding1,2, Ran Xiao1, Weijia Wang1
1Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, United States of America.
Physiological measurement
|March 26, 2024
概括
摄影电磁镜 (PPG) 和人工智能 (AI) 的最新进展为检测心房动 (AF) 提供了新的方法. 本综述涵盖了2019-2022年持续AF监测的数字健康解决方案.
科学领域:
- 生物医学工程 生物医学工程
- 数字健康数字健康
- 人工智能的人工智能
背景情况:
- 心房动 (AF) 是一种常见的心律失常,与中风和死亡率增加等严重健康问题有关.
- 摄影电磁镜 (PPG) 是一种成本效益高的技术,用于连续的AF监控,集成到可穿戴设备中.
- 之前对基于PPG的AF检测的审查于2019年6月之前结束,因此需要对最近的进展进行更新.
研究的目的:
- 从2019年7月至2022年12月提供PPG基础的AF检测近期发展的全面审查.
- 分析数字健康和人工智能解决方案在该领域的应用.
- 确定和评估在指定时间内发表的57项相关研究.
主要方法:
- 在科学数据库中进行系统的文献搜索.
- 对统计方法,传统机器学习和深度学习方法的深入评估.
- 对当前基于PPG的AF检测技术的挑战和局限性的分析.
主要成果:
- 在2019年7月至2022年12月期间公布的关于基于PPG的AF检测的57项相关研究的识别.
- 对用于检测AF的PPG信号的各种AI和数字健康策略的评估.
- 使用的常见统计,机器学习和深度学习技术的摘要.
结论:
- 自2019年以来,基于PPG的AF检测使用AI和数字健康已经取得了显著进展.
- 需要继续进行研究,以应对现有的挑战,并完善检测方法.
- 维护一个专门的网站,以跟踪这个快速发展的领域的最新研究和更新.
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