目的探究在左房扩大的心房颤动(AF)患者中,左房前壁基质改变对窦性心律下12导联心电图V1导联P波终末电势(PTFV1)和心房激动时间(AAT)的影响。方法入选行经导管心内膜消融术且心房扩大的AF患者98例[73.5%为阵发性AF]。将患者分为3组:前...目的探究在左房扩大的心房颤动(AF)患者中,左房前壁基质改变对窦性心律下12导联心电图V1导联P波终末电势(PTFV1)和心房激动时间(AAT)的影响。方法入选行经导管心内膜消融术且心房扩大的AF患者98例[73.5%为阵发性AF]。将患者分为3组:前壁线性消融组(20例),前壁低电压组(21例)和对照组(57例)。记录所有患者术后的常规12导联心电图,比较各组之间PTFV1和AAT。结果前壁线性消融组较对照组PTFV1减小,AAT延迟[PTFV1:(0.007±0.011)mm·s vs (0.034±0.038)mm·s,P=0.024;AAT:(152.8±40.9)ms vs(91.6±21.1)ms,P<0.001];前壁低电压组患者较对照组的PTFV1亦减小,AAT亦延迟[PTFV1:(0.008±0.014)mm·s vs (0.034±0.038)mm·s,P=0.048;AAT:(137.7±40.8)ms vs (91.6±21.1)ms,P<0.001]。前壁线性消融组和前壁低电压组的PTFV1和AAT差别无显著性。结论左房扩大的患者, PTFV1减小,AAT延长提示左房前壁基质异常。展开更多
目的:探讨左心房低电压区与心房颤动(房颤)导管消融术后复发的相关性。方法:系统检索建库至2020年9月3日PubMed、Embase、Cochrane Library、Web of Science、维普、中国知网、万方数据库及中国生物医学文献数据库关于左心房低电压区与...目的:探讨左心房低电压区与心房颤动(房颤)导管消融术后复发的相关性。方法:系统检索建库至2020年9月3日PubMed、Embase、Cochrane Library、Web of Science、维普、中国知网、万方数据库及中国生物医学文献数据库关于左心房低电压区与房颤导管消融术后复发相关性的研究。应用RevMan5.3软件制作森林图,应用Stata 12.0软件进行发表偏倚检验。结果:共纳入11项研究1832例房颤患者,平均年龄59.6岁,随访期间共441例复发。Meta分析结果显示左心房低电压区可显著增加房颤患者导管消融术后的复发风险(HR=2.13,95%CI:1.59~2.86)。结论:左心房低电压区与房颤导管消融术后的复发显著相关。展开更多
In the power distribution system,the missing or incorrect file of users-transformer relationship(UTR)in lowvoltage station area(LVSA)will affect the leanmanagement of the LVSA,and the operation andmaintenance of the d...In the power distribution system,the missing or incorrect file of users-transformer relationship(UTR)in lowvoltage station area(LVSA)will affect the leanmanagement of the LVSA,and the operation andmaintenance of the distribution network.To effectively improve the lean management of LVSA,the paper proposes an identification method for the UTR based on Local Selective Combination in ParallelOutlier Ensembles algorithm(LSCP).Firstly,the voltage data is reconstructed based on the information entropy to highlight the differences in between.Then,the LSCP algorithmcombines four base outlier detection algorithms,namely Isolation Forest(I-Forest),One-Class Support VectorMachine(OC-SVM),Copula-Based Outlier Detection(COPOD)and Local Outlier Factor(LOF),to construct the identification model of UTR.This model can accurately detect users’differences in voltage data,and identify users with wrong UTR.Meanwhile,the key input parameter of the LSCP algorithm is determined automatically through the line loss rate,and the influence of artificial settings on recognition accuracy can be reduced.Finally,thismethod is verified in the actual LVSA where the recall and precision rates are 100%compared with othermethods.Furthermore,the applicability to the LVSAs with difficult data acquisition and the voltage data error in transmission are analyzed.The proposed method adopts the ensemble learning framework and does not need to set the detection threshold manually.And it is applicable to the LVSAs with difficult data acquisition and high voltage similarity,which improves the stability and accuracy of UTR identification in LVSA.展开更多
文摘目的探究在左房扩大的心房颤动(AF)患者中,左房前壁基质改变对窦性心律下12导联心电图V1导联P波终末电势(PTFV1)和心房激动时间(AAT)的影响。方法入选行经导管心内膜消融术且心房扩大的AF患者98例[73.5%为阵发性AF]。将患者分为3组:前壁线性消融组(20例),前壁低电压组(21例)和对照组(57例)。记录所有患者术后的常规12导联心电图,比较各组之间PTFV1和AAT。结果前壁线性消融组较对照组PTFV1减小,AAT延迟[PTFV1:(0.007±0.011)mm·s vs (0.034±0.038)mm·s,P=0.024;AAT:(152.8±40.9)ms vs(91.6±21.1)ms,P<0.001];前壁低电压组患者较对照组的PTFV1亦减小,AAT亦延迟[PTFV1:(0.008±0.014)mm·s vs (0.034±0.038)mm·s,P=0.048;AAT:(137.7±40.8)ms vs (91.6±21.1)ms,P<0.001]。前壁线性消融组和前壁低电压组的PTFV1和AAT差别无显著性。结论左房扩大的患者, PTFV1减小,AAT延长提示左房前壁基质异常。
文摘目的:探讨左心房低电压区与心房颤动(房颤)导管消融术后复发的相关性。方法:系统检索建库至2020年9月3日PubMed、Embase、Cochrane Library、Web of Science、维普、中国知网、万方数据库及中国生物医学文献数据库关于左心房低电压区与房颤导管消融术后复发相关性的研究。应用RevMan5.3软件制作森林图,应用Stata 12.0软件进行发表偏倚检验。结果:共纳入11项研究1832例房颤患者,平均年龄59.6岁,随访期间共441例复发。Meta分析结果显示左心房低电压区可显著增加房颤患者导管消融术后的复发风险(HR=2.13,95%CI:1.59~2.86)。结论:左心房低电压区与房颤导管消融术后的复发显著相关。
文摘In the power distribution system,the missing or incorrect file of users-transformer relationship(UTR)in lowvoltage station area(LVSA)will affect the leanmanagement of the LVSA,and the operation andmaintenance of the distribution network.To effectively improve the lean management of LVSA,the paper proposes an identification method for the UTR based on Local Selective Combination in ParallelOutlier Ensembles algorithm(LSCP).Firstly,the voltage data is reconstructed based on the information entropy to highlight the differences in between.Then,the LSCP algorithmcombines four base outlier detection algorithms,namely Isolation Forest(I-Forest),One-Class Support VectorMachine(OC-SVM),Copula-Based Outlier Detection(COPOD)and Local Outlier Factor(LOF),to construct the identification model of UTR.This model can accurately detect users’differences in voltage data,and identify users with wrong UTR.Meanwhile,the key input parameter of the LSCP algorithm is determined automatically through the line loss rate,and the influence of artificial settings on recognition accuracy can be reduced.Finally,thismethod is verified in the actual LVSA where the recall and precision rates are 100%compared with othermethods.Furthermore,the applicability to the LVSAs with difficult data acquisition and the voltage data error in transmission are analyzed.The proposed method adopts the ensemble learning framework and does not need to set the detection threshold manually.And it is applicable to the LVSAs with difficult data acquisition and high voltage similarity,which improves the stability and accuracy of UTR identification in LVSA.
