基于Poincaré散点图和符号动力学的心电分析方法

心率变异性（heartrate variability，HRV）可以用于进行心脏相关疾病的预测、预防和预后评价等.结合心电散点图和符号动力学的方法，从ECG信号中提取HRV序列，绘制心电散点图，并对散点图中散点进行分区编号编码.计算不同编码的出现概率进而计算整个序列信息熵.以该熵值作为心电特征用于识别和分类.实验得到窦性心律和房颤心律的分类正确率为86.67%，窦性心律与伴有失常心律的早搏分类正确率为90%.证明该方法能有效分类窦性心律与失常心律. 

ECG Feature Analysis Based on Poincaré Plot and Symbolic Dynamics

Heart rate variability (HRV) can be used to predict, prevent heart related diseases and do prognosis evaluation. A method was proposed based on Poincaré plot and symbolic dynamics to analyze ECG feature. Firstly, HRV sequence was extracted from the ECG signal, and presented in Poincaré plot. Then, the splashes in different areas of the plot would be numbered and coded. The entropy of the ECG signal,calculated by the probability of each code, was applied to recognize and classify ECG signal as feature. The experiment results show that, the accuracy rates of classification in normal sinus rhythm and atrial fibrillation, normal sinus rhythm and premature beat are 86.67%, 90% respectively, the method can distinguish normal sinus rhythm from arrhythmia effectively.