心电动力学信号数据特征提取与模式识别的研究进展

Research progress of the data feature extraction and pattern recognition of cardiodynamicsgram signal

现代控制理论和计算机技术的进步,为深入研究心电信号内在动态病理特征创造了良好条件,并推动了相关学者在基于心电信号转化数据的神经网络模型构建、心电信号异质度特征的量化指标提取、心脏疾病辅助诊断等方面的早期探索。本文综述了心电动力学信号数据特征的研究现状,并介绍了心电动力学信号数据特征的提取方法、模式识别及其在心肌缺血辅助诊断、心肌缺血引发心脏疾病早期筛查和疗效评估等方面的应用。

With the development of modern control theory and computer technology,good conditions have been prepared for further research on the dynamic pathological features inherently in ECG signals.It also promotes early studies on neural network modeling of ECG signals,extraction of quantitative indicators characterizing the heterogeneity of ECG signals,auxiliary diagnosis of cardiac diseases,etc.This paper reviews literatures of the previous research on the data feature of cardiodynamicsgram signals,and introduces its extraction method,pattern recognition and its application in the auxiliary diagnosis of myocardial ischemia,early screening of cardiac diseases resulting from myocardial ischemia,evaluation of curative effect and so on.