• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

具有PP节律跟踪功能的P波检测器:在不同心律失常情况下的评估

P wave detector with PP rhythm tracking: evaluation in different arrhythmia contexts.

作者信息

Portet François

机构信息

Department of Computing Science, University of Aberdeen, Aberdeen AB24 3UE, UK.

出版信息

Physiol Meas. 2008 Jan;29(1):141-55. doi: 10.1088/0967-3334/29/1/010. Epub 2008 Jan 3.

DOI:10.1088/0967-3334/29/1/010
PMID:18175865
Abstract

Automatic detection of atrial activity (P waves) in an electrocardiogram (ECG) is a crucial task to diagnose the presence of arrhythmias. The P wave is difficult to detect and most of the approaches in the literature have been evaluated on normal sinus rhythms and rarely considered arrhythmia contexts other than atrial flutter and fibrillation. A novel knowledge-based P wave detector algorithm is presented. It is self-adaptive to the patient and able to deal with certain arrhythmias by tracking the PP rhythm. The detector has been tested on 12 records of the MIT-BIH arrhythmia database containing several ventricular and supra-ventricular arrhythmias. On the overall records, the detector demonstrates Se = 96.60% and Pr = 95.46%; for the normal sinus rhythm, it reaches Se = 97.76% and Pr = 96.80% and, in the case of Mobitz type II, it demonstrates Se = 72.79% and Pr = 99.51%. It also shows good performance for trigeminy and bigeminy, and outperforms some more sophisticated techniques. Although the results emphasize the difficulty of P wave detection in difficult arrhythmias (supra and ventricular tachycardias), it shows that domain knowledge can efficiently support signal processing techniques.

摘要

在心电图(ECG)中自动检测心房活动(P波)是诊断心律失常的一项关键任务。P波难以检测,并且文献中的大多数方法都是在正常窦性心律上进行评估的,很少考虑除心房扑动和颤动之外的其他心律失常情况。本文提出了一种新颖的基于知识的P波检测算法。它能自适应患者情况,并通过跟踪PP节律来处理某些心律失常。该检测器已在包含多种室性和室上性心律失常的麻省理工学院 - 贝斯以色列女执事医疗中心(MIT - BIH)心律失常数据库的12条记录上进行了测试。在所有记录中,该检测器的灵敏度(Se)为96.60%,阳性预测值(Pr)为95.46%;对于正常窦性心律,其灵敏度达到97.76%,阳性预测值为96.80%,在莫氏Ⅱ型情况下,其灵敏度为72.79%,阳性预测值为99.51%。它在三联律和二联律方面也表现良好,并且优于一些更复杂的技术。尽管结果强调了在困难心律失常(室上性和室性心动过速)中检测P波的难度,但它表明领域知识可以有效地支持信号处理技术。

相似文献

1
P wave detector with PP rhythm tracking: evaluation in different arrhythmia contexts.具有PP节律跟踪功能的P波检测器:在不同心律失常情况下的评估
Physiol Meas. 2008 Jan;29(1):141-55. doi: 10.1088/0967-3334/29/1/010. Epub 2008 Jan 3.
2
ECG interpretation: what is different in children?心电图解读:儿童有何不同?
Pediatr Nurs. 2001 May-Jun;27(3):224, 227-31.
3
An arrhythmia classification system based on the RR-interval signal.一种基于RR间期信号的心律失常分类系统。
Artif Intell Med. 2005 Mar;33(3):237-50. doi: 10.1016/j.artmed.2004.03.007.
4
Sensitivity and specificity of an automated external defibrillator algorithm designed for pediatric patients.一种专为儿科患者设计的自动体外除颤器算法的敏感性和特异性。
Resuscitation. 2008 Feb;76(2):168-74. doi: 10.1016/j.resuscitation.2007.06.032. Epub 2007 Aug 31.
5
Atrial wave detection algorithm for discovery of some rhythm abnormalities.用于发现某些节律异常的心房波检测算法。
Physiol Meas. 2007 May;28(5):595-610. doi: 10.1088/0967-3334/28/5/012. Epub 2007 Apr 30.
6
A wavelet-based ECG delineator: evaluation on standard databases.一种基于小波的心电图描记器:在标准数据库上的评估。
IEEE Trans Biomed Eng. 2004 Apr;51(4):570-81. doi: 10.1109/TBME.2003.821031.
7
Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal.基于支持向量机的心律失常分类,使用心率变异性信号的降维特征
Artif Intell Med. 2008 Sep;44(1):51-64. doi: 10.1016/j.artmed.2008.04.007. Epub 2008 Jun 27.
8
Temporal and spatial phase analyses of the electrocardiogram stratify intra-atrial and intra-ventricular organization.心电图的时空相位分析可对心房内和心室内组织进行分层。
IEEE Trans Biomed Eng. 2004 Oct;51(10):1749-64. doi: 10.1109/TBME.2004.827536.
9
Pediatric dysrhythmias.小儿心律失常
Pediatr Clin North Am. 2006 Feb;53(1):85-105, vi. doi: 10.1016/j.pcl.2005.10.004.
10
Automatic atrial tachyarrhythmia detection from intracardiac electrograms.从心内电图自动检测房性快速心律失常。
Ital Heart J. 2000 Jun;1(6):412-9.

引用本文的文献

1
Machine learning workflow for edge computed arrhythmia detection in exploration class missions.探索类任务中用于边缘计算心律失常检测的机器学习工作流程。
NPJ Microgravity. 2024 Jun 22;10(1):71. doi: 10.1038/s41526-024-00409-0.
2
An ECG Stitching Scheme for Driver Arrhythmia Classification Based on Deep Learning.基于深度学习的驾驶员心律失常分类的心电图拼接方案。
Sensors (Basel). 2023 Mar 20;23(6):3257. doi: 10.3390/s23063257.
3
Reliable P wave detection in pathological ECG signals.病理性 ECG 信号中的可靠 P 波检测。
Sci Rep. 2022 Apr 21;12(1):6589. doi: 10.1038/s41598-022-10656-4.
4
A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection.可穿戴心电图远程监测系统在心房颤动检测中的应用。
Sensors (Basel). 2020 Jan 22;20(3):606. doi: 10.3390/s20030606.
5
Advanced P Wave Detection in Ecg Signals During Pathology: Evaluation in Different Arrhythmia Contexts.心电图信号中病理期间的 P 波提前检测:不同心律失常情况下的评估。
Sci Rep. 2019 Dec 13;9(1):19053. doi: 10.1038/s41598-019-55323-3.
6
Atrial Fibrillation Detection from Wrist Photoplethysmography Signals Using Smartwatches.基于智能手表的腕部光电容积脉搏波信号心房颤动检测
Sci Rep. 2019 Oct 21;9(1):15054. doi: 10.1038/s41598-019-49092-2.