计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 210900144-8.doi: 10.11896/jsjkx.210900144
韩宇森1, 杨宏波2, 孙静1, 潘家华2, 王威廉1
HAN Yu-sen1, YANG Hong-bo2, SUN Jing1, PAN Jia-hua2, WANG Wei-lian1
摘要： 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20 s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%。
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