正确评价心脏舒张功能障碍及其严重程度对射血分数保留型心衰的临床诊断和治疗有重大意义，但目前尚缺乏简明和准确的舒张功能无创检测方法，鉴于心音与心脏血流动力关系紧密，能够反映影响心室舒张的主要因素包括心室弛张和顺应性的改变，因此，本项目提出基于心音幅值-间期序列多模态耦合分析的舒张功能评估方法研究，通过探究心音多模态特征与舒张功能障碍分级之间的关系，建立用于评估舒张功能的心音指标；通过将心音幅值序列和间期序列等一维序列组合成二维矩阵，采用卷积神经网络获得全连接，将其与心音多模态特征参数进行信息融合，最后通过机器学习的方法对心脏舒张功能异常进行检测，实现基于计算机辅助诊断的心脏舒张功能障碍分级，并应用于射血分数保留型心衰的鉴别。本项目的理论研究填补了从心脏变力性检测角度评估舒张功能的研究空缺，为心脏机械生理活动分析及其应用的研究提供了新思路和新方法，具有重要的理论意义和广阔的应用前景。

It is of great significance for the diagnosis of heart failure with preserved ejection fraction to evaluate diastolic function and detect the degree of diastolic dysfunction, but now the existing method can not provide noninvasive, convenient and accurate measurement for diastolic function. Since heart sounds are closely related to cardiac hemodynamics, they could reflect the changes of ventricular relaxation and compliance that are the main influence on ventricular diastole. Therefore, this project propose study on diastolic function evaluation method based on heart sound amplitude-interval sequence multi-modal coupling analysis. Firstly, the relationship between heart sound multi-modal features and diastolic dysfunction classification will be investigated, to find the heart sound indexes which are used to evaluate the diastolic function. Then, the two-dimensional matrix makes up of several one-dimensional sequences such as the heart sound amplitude sequences and interval sequences is input into convolutional neural network, and then the obtained full connection and heart sound multi-modal features are integrated. Finally, machine learning will be used to detect the diastolic disorder to realize the classification of left ventricular diastolic dysfunction as well as the diagnosis of heart failure with preserved ejection fraction. The theoretical research of this project fills the vacancy of study that evaluates diastolic function from the perspective of cardiac inotropic action, and provides new idea and method for the study of the analysis of cardiac mechanical physiological activity as well as its application, which have important theoretical significance and broad application outlooks.