Cardiac output (CO) refers to the amount of blood ejected from a unilateral ventricle per minute and is an important measure of cardiac function. Thermodilution is the gold standard for CO measurement because of its accuracy. However, the traditional thermodilution method requires calibration of the correction factor before measurement, which makes its practical application difficult. Therefore, conducting CO measurement by using a machine-learning-based thermodilution method is proposed in this paper, and CO is regressed and predicted through the thermodilution curve by a machine learning model. In this paper, we constructed five cardiac vascular models, and three of them were randomly selected to simulate the thermodilution process. Nine features of the thermodilution curve from the time–frequency domains were extracted and fed into the multilayer perceptron model for training. On the basis of a cross-validation method, the accuracy of the final prediction model was 97.99% (±1.94%). Simultaneously, a trained neural network was used to predict the CO of the remaining two cardiac vascular models, and the resulting error was within 5%. In this paper, an experimental system consisting of a water pump, a three-way valve and a temperature sensor is also designed, and the thermodilution curves at different quantities of flow are tested and regressed and predicted with the above model, with the error being within 10%, which met the requirement for real-world use, and thus, a method was established for measuring CO by using machine-learning-based thermodilution.

中文翻译：

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基于机器学习通过热稀释法测量心输出量

心输出量（CO）是指每分钟从单侧心室射出的血液量，是衡量心脏功能的重要指标。由于其准确性，热稀释法是 CO 测量的黄金标准。然而，传统的热稀释法需要在测量前对校正因子进行校准，使其实际应用困难。因此，本文提出采用基于机器学习的热稀释法进行CO测量，并通过机器学习模型通过热稀释曲线对CO进行回归和预测。在本文中，我们构建了五个心脏血管模型，并随机选择其中三个模型来模拟热稀释过程。从时频域中提取热稀释曲线的九个特征并将其输入多层感知器模型进行训练。在交叉验证方法的基础上，最终预测模型的准确率为97.99%（±1.94%）。同时，使用经过训练的神经网络对其余两个心血管模型的 CO 进行预测，得到的误差在 5% 以内。本文还设计了一个由水泵、三通阀和温度传感器组成的实验系统，利用上述模型对不同流量下的热稀释曲线进行了测试和回归预测，误差为10%以内，满足实际使用要求，因此建立了一种基于机器学习的热稀释法测量CO的方法。