Implementation and Calibration of a Deep Neural Network to Predict Parameters of Left Ventricular Systolic Function Based on Pulmonary and Systemic Arterial Pressure Signals

Frontiers in Physiology

Lucas Liaudet¹  Luca Pegolotti²  Jean Bonnemain²  Simone Deparis² 

[1] Adult Intensive Care and Burn Unit, University Hospital and University of Lausanne, Lausanne, Switzerland;SCI-SB-SD, School of Basic Sciences, Ecole Polytechnique Fédérale de Lausanne, Institute of Mathematics, Lausanne, Switzerland;

关键词: heart failure;    hemodynamics;    deep neural network;    cardiovascular modeling;    blood flow model;    machine learning;   

DOI  :  10.3389/fphys.2020.01086

来源: DOAJ

【 摘 要 】

The evaluation of cardiac contractility by the assessment of the ventricular systolic elastance function is clinically challenging and cannot be easily obtained at the bedside. In this work, we present a framework characterizing left ventricular systolic function from clinically readily available data, including systemic and pulmonary arterial pressure signals. We implemented and calibrated a deep neural network (DNN) consisting of a multi-layer perceptron with 4 fully connected hidden layers and with 16 neurons per layer, which was trained with data obtained from a lumped model of the cardiovascular system modeling different levels of cardiac function. The lumped model included a function of circulatory autoregulation from carotid baroreceptors in pulsatile conditions. Inputs for the DNN were systemic and pulmonary arterial pressure curves. Outputs from the DNN were parameters of the lumped model characterizing left ventricular systolic function, especially end-systolic elastance. The DNN adequately performed and accurately recovered the relevant hemodynamic parameters with a mean relative error of less than 2%. Therefore, our framework can easily provide complex physiological parameters of cardiac contractility, which could lead to the development of invaluable tools for the clinical evaluation of patients with severe cardiac dysfunction.

【 授权许可】

Unknown