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dc.contributor.advisor席斌
dc.contributor.author吴璇
dc.date.accessioned2017-06-20T08:44:45Z
dc.date.available2017-06-20T08:44:45Z
dc.date.issued2016-03-18
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/134572
dc.description.abstract心律失常一直是威胁人类健康的大敌,心房颤动(房颤)则是其中最常见的一种。心电信号是诊断心律失常的最佳手段,目前心律失常的诊断仍是有经验的医生观察心电图进行判断,因此心电信号的自动分析处理和心律失常自动检测成为当前信号处理领域的研究热点之一,其进步和发展对医疗事业和人类健康水平的提高具有重大意义。 本文对心电信号产生机理及其与心律失常的关系做了详细分析,在研究基于RR间期的心律失常检测算法的基础上,提出了一种新的算法设计思路。 心电信号中信息量巨大,且一次性分类所有心律失常十分困难,本文对比分析了两种基于RR间期的心律失常检测算法。前者采用RR间期和QRS波宽作为一级分类指标,粗略分类部分心...
dc.description.abstractCardiac arrhythmias always threaten human health, and atrial fibrillation (AF) is one of the most popular arrhythmias. The electrocardiogram (ECG) signal is the best method of diagnosing arrhythmias. However, diagnosing arrhythmias relies on experienced doctors' explanation for ECG signals. So the auto processing of ECG signals as well as auto deteciton of arrhythmias has been one of the hot resea...
dc.language.isozh_CN
dc.relation.urihttp://210.34.4.28/opac/openlink.php?strText=50410&doctype=ALL&strSearchType=callno
dc.source.urihttp://210.34.4.13:8080/lunwen/detail.asp?serial=50039
dc.subject心房颤动检测
dc.subjectRR间期
dc.subjectAR模型
dc.subject时间序列分段
dc.subjectAtrial Fibrillation Detection
dc.subjectRR interval
dc.subjectAR model
dc.subjectTime Series Segmentation
dc.titleRR间期分段与房颤检测算法研究
dc.title.alternativeRR Interval Time Series Segmentation and Atrial Fibrillation Detection
dc.typethesis
dc.date.replied2015-05-20
dc.description.note学位:工学硕士
dc.description.note院系专业:信息科学与技术学院_模式识别与智能系统
dc.description.note学号:23220121153051


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