Abstract：
Heart failure (HF) is defined as the inability of the heart to meet body oxygen demand requiring an elevation in left ventricular filling pressures (LVP) to compensate. LVP increase can be assessed in the cardiac catheterization laboratory, but this procedure is invasive and time-consuming to the extent that physicians rather rely on non-invasive diagnostic tools. In this work, we assess the feasibility to develop a novel machine-learning (ML) approach to predict clinically relevant LVP indices. Synchronized invasive (pressure-volume tracings) and non-invasive signals (ECG, pulse oximetry, and cardiac sounds) were collected from anesthetized, closed-chest Göttingen minipigs. Animals were either healthy or had HF with reduced ejection fraction and circa 500 heartbeats were included in the analysis for each animal. The ML algorithm showed excellent prediction of LVP indices estimating, for instance, the end-diastolic pressure with a R2 of 0.955. This novel ML algorithm could assist clinicians in the care of HF patients.
摘要：
心力衰竭（HF）定义为心脏无法满足身体的需氧量，需要提高左心室充盈压（LVP）来补偿。LVP增加可以在心导管实验室评估，但这一过程是侵入性和耗时的，以至于医生相当依赖非侵入性诊断工具。在这项工作中,我们评估了开发新的机器学习(ML)方法来预测临床相关LVP指数的可行性.同步侵入性(压力-容积描记)和非侵入性信号(ECG,脉搏血氧饱和度,和心音)从麻醉中收集，闭胸哥廷根小型猪。动物是健康的或患有具有降低的射血分数的HF,并且在对每只动物的分析中包括大约500次心跳。ML算法对LVP指数估计的预测效果很好，例如,舒张末期压的R2为0.955。这种新颖的ML算法可以帮助临床医生护理HF患者。
