你的智能手机可以充当脉冲氧计和单导电心电图
Ahsan Mehmood1, Asma Sarouji1, M Mahboob Ur Rahman2
1Department of Electrical Engineering, KAUST, Thuwal, Kingdom of Saudi Arabia.
Scientific reports
|November 7, 2023
概括
这项研究开发了一个智能手机应用程序,使用深度学习来监测脉,血液氧气和呼吸率等生命体征. 它还提取心电图 (ECG) 信号,用于早期检测心脏病.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 数字卫生技术数字卫生技术
背景情况:
- 后COVID-19时代需要可访问的工具,以进行持续的健康监测.
- 智能手机为开发低成本,非侵入性诊断解决方案提供了一个无处不在的平台.
- 目前用于生命体征监测的方法可能昂贵且难以获得.
研究的目的:
- 为智能手机开发基于深度学习的系统,用于测量脉冲率 (PR),血液氧和度 (SpO2) 和呼吸速率 (RR).
- 创建一种使用智能手机视频录制来提取单导电心电图 (ECG) 信号的方法.
- 为了实现快速的,自我管理的健康评估和心血管疾病的早期检测.
主要方法:
- 利用深度学习,包括卷积神经网络 (CNN),视觉转换器和CLIP模型,来分析智能手机视频-光电显微镜 (vPPG) 信号.
- 开发了一种新的离散等号变换和前神经网络方法,用于从vPPG数据中提取ECG信号.
- 试验对象使用智能手机摄像头记录指尖视频进行信号处理和分析.
主要成果:
- 从高精度的vPPG信号中成功估计了PR,SPO2和RR.
- 证明了从智能手机视频录制生成单导电图信号的可行性.
- 验证了早期检测心律失常的潜力,如心房动.
结论:
- 基于智能手机的深度学习模型可以有效地监测关键生命体征并获取心电图数据.
- 这项技术提供了一个低成本,易于使用的工具,用于持续的健康自我监测和早期疾病检测.
- 拟议的系统可以减少医疗负担,并可能降低医疗保险成本.
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