使用PPG信号进行非侵入性动脉血压测量和SpO估计:一个深度学习框架
Yan Chu1, Kaichen Tang1, Yu-Chun Hsu1
1McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
这项研究介绍了一种基于变压器的深度学习模型,使用光聚体图 (PPG) 信号来准确,非侵入性地监测慢性疾病患者的血压和氧和度.
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
- 心血管监测 心血管监测
背景情况:
- 监测血压和氧和度对于慢性疾病管理至关重要,特别是高血压和血管疾病.
- 目前用于测量动脉血压的侵入性方法存在不适和感染等风险.
- 在临床实践中,非常需要非侵入性替代品.
研究的目的:
- 开发一种非侵入性方法,用光电图 (PPG) 信号来估计动脉血压和氧和.
- 为个性化生理测量提出一种基于变压器的新型深度学习架构.
- 为当前的侵入性监测技术提供舒适安全的替代方案.
主要方法:
- 使用了从非侵入性设备中收集的光电图 (PPG) 信号.
- 开发并应用基于Transformer的深度学习架构用于数据分析.
- 在MIMIC III数据集中的1,732个受试者的子集上训练并验证了模型.
主要成果:
- 达到平均绝对误差为2.52±2.43 mmHg对于静缩血压和1.37±1.89 mmHg对于静缩血压.
- 获得了0.58±0.79%的氧和平均绝对误差.
- 满足了医学仪器进步协会 (AAMI) 的标准,并实现了英国高血压协会 (BHS) 标准的A级.
结论:
- 拟议的深度学习模型符合严格的临床准确性标准.
- 这种非侵入性方法有可能显著提高血压和氧和度测量的准确性.
- 这项技术有望通过更精确,更为患者友好的监测解决方案来加强医疗保健服务.
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