基于复杂网络映射的房颤脉检测  

Atrial Fibrillation Pulse Detection via Complex Network Method

作　　者：李晗[1] 赵海[1] 陆育卉 邵士亮[2] 

机构地区：[1]东北大学计算机科学与工程学院,沈阳110819 [2]中国科学院沈阳自动化研究所机器人学国家重点实验室,沈阳110819

出　　处：《计算机科学》2017年第6期237-239,273,共4页Computer Science

基　　金：国家自然科学基金资助项目(61101121)资助

摘　　要：为了探索脉搏波中蕴含的复杂性及简便快速地检测心房颤动,结合中国传统医学中"房颤脉"的概念,设计了一种基于复杂网络的房颤脉检测方法。将光电容积脉搏波的时间序列按可视图法映射成复杂网络,将平均心率与复杂网络的度分布作为支持向量机的输入,基于高斯径向核函数设计了二分类的支持向量机。针对阵发性房颤患者的实验表明,这种方法可以有效地分辨病人的发病状态和正常状态。In order to explore the complexity of pulse wave,combined with the concept of＂atrial fibrillation pulse＂in traditional Chinese medicine,a complex network method to detect atrial fibrillation was presented.The photoplethysmograph pulse wave is thereby transformed to a network topology using visibility graph method.A binary classification support vector machine（SVM）based on Gausssian kernel function is designed to distinguish between normal sinus rhythm and atrial fibrillation.The degree distribution of the network and the average heart rate are extracted as the input features of the SVM.According to the experimental results of patients with paroxysmal atrial fibrillation,this method can effectively identify the patient＇s disease status and normal status.

关 键 词：脉搏波 阵发性房颤 复杂网络 可视图法 支持向量机 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]