An intelligent aortic valve model for complete cardiac cycle

Abstract

The aortic valve (AV) is crucial for cardiovascular (CV) hemodynamic, impacting cardiac output (CO) and left ventricular volumetric flow rate (LVQ). Its nonlinear behavior challenges standard LVQ prediction methods as well as CO one. This study presents a novel approach for modeling the AV in the CV system, offering an improved method for estimating crucial parameters like LVQ across various AV conditions, including aortic stenosis (AS). The model, based on AV channel length during the entire cardiac phase, introduces a time-varying AV resistance (TV-AVR) parameterized by the pressure ratio across the AV and LVQ, enabling the simulation of both healthy and AS-related conditions. To validate this model, in vitro measurements are compared using a hybrid mock circulatory loop device. An unconventional use of a convolutional neural network (CNN) corrects the model's estimates, eliminating the need for labeled datasets. This approach, incorporating real-time learning and transforming 1-D CV signals into 2-D tensors, significantly improves the accuracy of LVQ measurements, achieving an error rate of less than 3.41 +/- 4.84% for CO in healthy conditions and 2.83 +/- 1.35% in AS cases-a 33.13% enhancement over linear diode models. These results underscore the potential of this approach for enhancing the diagnosis, prediction, and treatment of AV diseases. The key contributions of the proposed method encompass nonlinear TV-AVR estimation, investigation of transient CV responses, prediction of instantaneous CO, development of a flexible framework for noninvasive measurements integration, and the introduction of an adjustable resistance model using an extended Kalman filter (EKF) and CNN combination, all without requiring labeled data. This study introduces a novel approach for modeling the aortic valve (AV) in the cardiovascular system (CVS), crucial for estimating left ventricular volumetric flow rate (LVQ) across various AV conditions including aortic stenosis (AS). The model utilizes a time-varying AV resistance (TV-AVR) parameterized by pressure ratios, validated through in vitro measurements, and further enhanced using a convolutional neural network (CNN) for real-time learning. Results show a significant improvement in accuracy for LVQ measurements, highlighting the potential of this approach for AV disease diagnosis and treatment. image