摘要
阵发性房颤(PAF)是一种具有偶发性的心律失常,其较高的漏检率导致心脏相关疾病的增加。该文提出了一种基于核稀疏编码的自动检测方法,可以仅根据较短RR间期数据识别PAF发作。该方法采用特殊几何结构来分析数据高维特性,通过计算协方差矩阵作为特征描述子,找到蕴含在数据中的黎曼流形结构;然后基于Log-Euclid框架,利用核方法将流形空间映射到高维可再生核希尔伯特空间,以获取更准确的稀疏表示来快速识别PAF。经麻省理工学院-贝斯以色列医院房颤数据库验证,获得98.71%的敏感性、98.43%的特异度和98.57%的总准确率。因此,该研究对检测短暂发作的PAF有实质性的改善,在临床监测和治疗方面显示出良好的潜力。
Paroxysmal Atrial Fibrillation(PAF) is a kind of accidental arrhythmia, and its high missed detection rate leads to the increase of heart-related diseases. An automatic detection method is proposed based on kernel sparse coding, which can identify PAF attacks based only on short RR interval data. A special geometric structure is presented to analyze the high-dimensional characteristics of the data, and the covariance matrix is calculated as a feature descriptor to find the Riemannian manifold structure contained in the data;Based on the Log-Euclidean framework, a manifold method is used to map the manifold space to a highdimensional renewable kernel Hilbert space to obtain a more accurate sparse representation to identify quickly PAF. After verification by the Massa-chusetts Institute of Technology-Beth Israel Hospital atrial fibrillation database, the sensitivity is 98.71%, the specificity is 98.43%, and the total accuracy rate is 98.57%. Therefore,this study has a substantial improvement in the detection of transient PAF and shows good potential for clinical monitoring and treatment.
作者
刘明
孟宪辉
熊鹏
刘秀玲
LIU Ming;MENG Xianhui;XIONG Peng;LIU Xiuling(College of Electronic and Information Engineering,Hebei University,Baoding 071002,China;Key Laboratory of Digital Medical Engineering of Hebei Province,Baoding 071002,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2020年第7期1743-1749,共7页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61673158)
河北省自然科学基金(F2018201070)
河北省研究生创新资助项目(CXZZSS2019006)
河北省青年拔尖人才项目(BJ2019044)。
关键词
阵发性房颤
协方差描述子
黎曼流形
核稀疏编码
Paroxysmal Atrial Fibrillation(PAF)
Covariance descriptor
Riemann manifold
Kernel sparse coding