先天性心脏病筛查系统设计

Design of a congenital heart disease screening system

为提高医生筛查先天性心脏病的效率,设计一款基于卷积神经网络的先天性心脏病筛查系统。系统以软硬协同的方式实现心音、心电等生理参数的实时同步采集以及可视化和定量化分析。系统包含上下位机,下位机以FPGA为核心实现心音心电数据采集以及小波阈值去噪等预处理,上位机在Windows系统环境下以Python编程语言实现二阶谱特征提取、卷积神经网络二分类识别以及用户界面可视化显示。最终,系统对200名志愿者进行测试,准确率达到94.5%,特异度为95.9%,敏感度为93.2%。结果表明系统具有良好的表现,可以为临床先心病筛查提供有效的辅助。

A congenital heart disease screening system based on convolutional neural network is designed for improving the efficiency of doctors in screening for congenital heart disease.The system realizes the real-time synchronous acquisition,visualization and quantitative analysis of physiological parameters such as heart sounds and ECG data with the cooperation of software and hardware.The system includes upper and lower computers.The lower computer uses FPGA as the core to complete the preprocessing such as heart sound and ECG data acquisition and wavelet threshold denoising,while the upper computer implements the second-order spectral feature extraction,the binary classification using convolutional neural network,and the visual display on the user interface in the Python programming language under the Windows environment.The established screening system is tested on 200 volunteers,and finally achieves an accuracy rate of 94.5%,a specificity of 95.9%,and a sensitivity of 93.2%,indicating that the system has good performance and can provide effective assistance for clinical diagnosis of congenital heart disease.