基于心电信号的先心病肺动脉高压识别分类研究

Study on Recognition and Classification of Congenital Heart Disease and Pulmonary Hypertension Based on ECG Signal

先天性心脏病相关性肺动脉高压(Pulmonary Arterial Hypertension,PAH)在临床上有着很高的发病率、致残率和病死率,其确诊主要采用右心导管测量平均肺动脉压,这种方法有创且操作性要求高,不便在筛查中采用,因此探索一种非介入式CHD-PAH智能辅助诊断方案意义重大。在先心病的基础上对CHD-PAH进行研究,从分析ECG入手,通过预处理、心拍分割、波形检测、特征提取、数据扩充、分类识别等手段对CHD-PAH进行建模预测。在Christov_segmenter算法基础上,利用差分阈值和局部峰值改进,检测QRS波、P波和T波,最后提取基于时间和幅度的双模态特征。为了拟合出最佳的分类模型,实验采用了支持向量机、随机森林及K邻近等分类器,并设计基于T分布的麻雀搜索算法改进支持向量机。实验共使用460段时长为20s的1导联ECG信号进行训练和测试。实验结果表明,所提算法优化的支持向量机模型预测准确率、特异度和灵敏度分别可达99.76%,99.80%,99.73%。

Pulmonary arterial hypertension(PAH)associated with congenital heart disease has a high clinical morbidity,disability and mortality.Its diagnosis is mainly made by measuring the mean pulmonary arterial pressure by right heart catheterization.This method is invasive and has high operational requirements,and it is inconvenient to be used in screening,so it is of great significance to explore a non-invasive CHD-PAH intelligent auxiliary diagnosis scheme.This paper studies CHD-PAH on the basis of congenital heart disease,starting from the analysis of ECG signal,and modeling and predicting CHD-PAH by means of preprocessing,heart beat segmentation,waveform detection,feature extraction,data expansion,classification and identification.Based on the Christov_segmenter algorithm,the differential threshold and local peak improvement are used to detect QRS waves,P waves and T waves,and finally extract bimodal features based on time and amplitude.In order to fit the best classification model,the support vector machine,random forest and K-neighbor classifiers are used in experiments,and a sparrow search algorithm based on T distribution is designed to improve the support vector machine.A total of 4601-lead ECG signals with a duration of 20sare used for training and testing.Experimental results show that the prediction accuracy,specificity and sensitivity of the SVM model optimized by the proposed algorithm can reach 99.76%,99.80%and 99.73%,respectively.