冠心病无创检测新方法及系统设计

基金项目:

北京市教委基金（KB00190）.

详细信息

A New Method and System Designed for CAD Noninvasive Detecting

摘要: 提出了一种基于BP型人工神经网络的冠心病无创检测新方法,并设计了相应的诊断系统.该系统对人体体表心电信号进行处理,提取与冠心病有关的特征参数(RR间期,QRS波宽度,ST段电平和斜率),结合血压及个体基本信息(性别、年龄、体质量、是否吸烟等),利用BP型人工神经网络进行冠心病的无创检测和诊断.实验结果表明该方法具有一定的可操作性和实用性.
- 特征提取 /
- 无创检测 /
- 神经网络
Abstract: A new method for noninvasive detecting coronary artery disease(CAD) based on BP artificial neural network (ANN) is presented, and the diagnosis system is designed. The ECG signals from body surface are proceeded, and four character parameters (RR interval, width of QRS complex, scope and slope of ST segment) of the ECG, correlating with CAD, are computed. CAD is detected and diagnosed by using BPANN, which is imputed with character parameters of the ECG, blood pressure and basic information (sex, age, weight, smoking or not). Experiment results show that the method mentioned in this paper may be applied and easily manipulated for CAD diagnosis.
- feature extraction /
- noninvasive medical procedures /
- neural network