[1]陆宏伟,钟高艳,龙芋帆,等. 全数据库评估概率密度函数法利用R-R间期检测房颤精度[J].中国医学物理学杂志,2018,35(3):333-337.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.016]
　LU Hongwei,ZHONG Gaoyan,LONG Yufan,et al. Evaluation of the precision of probability density function method using R-R intervals for detecting atrial fibrillation from the whole MIT-BIH arrhythmia database[J].Chinese Journal of Medical Physics,2018,35(3):333-337.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.016]
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 全数据库评估概率密度函数法利用R-R间期检测房颤精度()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
35卷
期数:
2018年第3期
页码:
333-337
栏目:
医学信号处理与医学仪器
出版日期:
2018-03-20

文章信息/Info

Title:
 Evaluation of the precision of probability density function method using R-R intervals for detecting atrial fibrillation from the whole MIT-BIH arrhythmia database
文章编号:
1005-202X（2018）03-0333-05
作者:
 陆宏伟; 钟高艳; 龙芋帆; 刘章财
 上海理工大学医疗器械与食品学院， 上海 200093
Author(s):
 LU Hongwei;  ZHONG Gaoyan;  LONG Yufan;  LIU Zhangcai
 School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
关键词:
 ; 房颤; R-R间期; 概率密度函数法; 早搏; 心律失常
Keywords:
 ; atrial fibrillation;  R-R intervals;  probability density function method;  premature heart beats;  arrhythmia
分类号:
R318.04；R318.5
DOI:
DOI:10.3969/j.issn.1005-202X.2018.03.016
文献标志码:
A
摘要:
 研究整个MIT-BIH心律失常数据库评估概率密度函数法利用R-R间期检测房颤的精度。研究发现正常窦性心律含较多早搏时[（早搏次数/总心搏次数）>9.3%]，识别房颤的精度下降到约70%；左束支传导阻滞含较多早搏时，识别房颤的精度下降到约80%；而房颤心电中频繁早搏对辨别房颤精度影响很小，仍达91%。可见该算法适用于区分关联性强的序列与关联性弱的序列。正常窦性心律和左束支传导阻滞心律相邻R-R间期关联性强，频繁早搏使其相邻R-R间期关联性减弱，从而降低识别房颤的精度；而房颤相邻R-R间期无关联性，频繁早搏对检测精度无影响。尽管数据库中有种类繁多的心律失常，且伴有频繁的早搏，算法全数据库共约110 000次心跳辨别房颤精度达82%~86%。
Abstract:
 The precision of probability density function method for detecting atrial fibrillation (AF) by R-R intervals is evaluated with the use of the whole MIT-BIH arrhythmia database. With the normal sinus rhythm with frequently occurring premature heart beats (PHB), the number of PHB/the total number of heart beats larger than 9.3%, AF detection precision is reduced to about 70%; with the left bundle branch block with frequently occurring PHB, the precision is decreased to 80%; while AF with frequently occurring PHB has little effects on the AF detection precision, and the detection precision remains 91%. Therefore, the proposed algorithm is suitable to differentiate sequences with high correlation from sequences with weak correlation. The neighboring R-R intervals of normal sinus rhythm and left bundle branch block are highly correlated. Frequently occurring PHB weakens the correlation among neighboring R-R intervals, which degrades the AF detection precision. However, there are no correlations among neighboring R-R intervals of AF, which indicated that frequently occurring PHB has no effects on the AF detection precision. The algorithm is evaluated with the whole MIT-BIH arrhythmia database which contains about 110,000 heart beats. Though many kinds of arrhythmia accompanied with frequently occurring PHB are included in the database, the AF detection precision of the whole database remains 82%-86%.

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备注/Memo

备注/Memo:
 【收稿日期】2017-10-22
【基金项目】上海高校特聘教授（东方学者）岗位计划资助
【作者简介】陆宏伟，讲师，研究方向：医学信号处理与智能化医学仪器，E-mail: luhongwei_mail@163.com
更新日期/Last Update: 2018-03-21