可穿戴的ECG-PPG深度学习模型用于基于心脏指数的非侵入性心脏输出估计心脏手术患者.
Minwoo Kim1, Min Dong Sung2, Jimyeoung Jung1
1MEZOO Co., Ltd., 200, Gieopdosi-ro, Jijeong-myeon, Wonju-si 26354, Republic of Korea.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
使用深度学习的可穿戴传感器可以非侵入性地估计心脏输出 (CO). 心脏指数正常化提高了准确性,支持在没有侵入性导管的情况下进行持续的血液动力学监测.
科学领域:
- 生物医学工程 生物医学工程
- 心血管生理学心血管生理学
- 人工智能在医学中的应用
背景情况:
- 准确的心力输出 (CO) 测量对于血液动力学管理至关重要,但通常需要侵入性监测.
- 持续的,非医院的CO监测是目前的侵入性方法所限制的.
- 可穿戴传感器与深度学习相结合,提供了一个有希望的非侵入性替代方案.
研究的目的:
- 开发和验证一种轻量级的深度学习模型,使用可穿戴式心电图 (ECG) 和光电图 (PPG) 信号进行非侵入性CO预测.
- 评估基于心脏指数 (CI) 的正常化是否提高了CO预测模型的性能.
- 为了实现连续的,无导管的血液动力学监测.
主要方法:
- 开发了一个深度学习模型,使用来自27名心脏手术患者的同时ECG和PPG信号.
- 训练了三个模型:直接的CO预测,间接的CO预测和CI预测.
- 模型的性能与肺动脉导管热稀释参考值进行了评估.
主要成果:
- 心脏指数 (CI) 模型表现最好.
- 间接CO预测模型显示错误指标 (MAE,RMSE,偏差) 的显著降低 (p < 0.0001).
- 间接CO估计实现了0.904的皮尔森相关系数和23.75%的百分比误差,达到<30%的基准.
结论:
- 可穿戴式ECG-PPG融合深度学习可实现准确的,非侵入性的心脏输出估计.
- 心脏指数的正常化增强了与侵入性测量模型的一致性.
- 这项技术支持连续的,无导管的血液动力学监测.
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