深度学习预测了心脏衰竭中地震心脏图信号的心脏输出
Jesse Wang1, Seyed M Nouraie2, Neil J Kelly3
1University of Pittsburgh School of Medicine, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA; University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
使用地震心电图 (SCG) 和心电图 (ECG) 的深度学习模型可以非侵入性地估计心脏输出 (CO). 这种方法对监测心力衰竭患者,特别是低输出状态的患者来说是有前途的.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 人工智能的人工智能
背景情况:
- 准确的心脏输出 (CO) 测量对于管理心血管损害至关重要.
- 标准的方法,如右心导管 (RHC) 是侵入性的,并有局限性.
- 地震心脏图 (SCG) 通过感知心脏机械活动提供了一个非侵入性的替代方案.
研究的目的:
- 开发和评估一个深度学习模型,用SCG,ECG和BMI来估计CO.
- 评估模型在接受RHC.心力衰竭患者的表现.
主要方法:
- 在73名心力衰竭患者的开放访问数据集上训练了一个深度卷积神经网络.
- 数据包括同时进行的RHC,SCG和ECG记录.
- 模型性能通过交叉验证对64名患者进行了验证.
主要成果:
- 该模型实现了CO (<6 L/min) 的平均偏差为-0.01 L/min,心脏指数 (<2.2 L/min/m2) 的平均偏差为0.07 L/min/m2.
- 协议的限制为CO -0.88至0.87 L/min,心脏指数为0.35至0.48 L/min/m2.
- 在低产量州的表现特别强.
结论:
- 使用可穿戴SCG传感器的深度学习可以非侵入性地估计CO.
- 基于SCG的监测具有潜在的临床决策潜力,在侵入性方法不切实际的情况下.
- 需要进一步的多中心验证来确认可通用性.
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