简介:Prematureventricularcontraction(PVC)isthemostfrequentarrhythmiaencounteredinclinicalpractice.PVCmayoccurinhealthsubjects,whichisnotimminentlylife-threateningbutmayrequiretherapiestopreventfurtherproblems.So,thetimelyPVCrecognitionbecomesveryimportantfortheanalysisofelectrocardiogram(ECG),especiallyfortheremoteECGmonitoringusingmobilephones.Inthispaper,aconstructionmethodofpersonalizedECGtemplateandaPVCrecognitionmethodbasedontemplatematchingwerestudied.Firstly,weselected43ECGrecordingsfromtheMIT-BIHarrhythmiadatabase.Allrecordingsweredividedintotwodatasets(DS1fortrainingandDS2fortesting)andeachdatasetapproximatelycontainedthesameproportionofPVCbeats.Subsequently,foreachrecording(30min)inDS1,thefirst5minrecordingswereusedtoconstructthepersonalizedECGtemplateandthelast25minrecordingswereusedfortheR-wavepeaksdetectionandPVCrecognition,wherethetemplatematchingmethodwereused.ThevalidityoftheproposedmethodswastestedusingDS2.Theresultsshowedthat:1)highbeatdetectionaccuracywasachievedforbothPVCbeatsandnon-PVCbeats;2)thesensitivityandspecificityofPVCrecognitionwere99.11%and99.96%forthefirst5minrecordingsrespectively,99.17%and99.43%forthelast25minrecordingsrespectively.Alltheproposedmethodscanbereal-timeperformed,whichshowapromisingprospectfortheapplicationofECGmobilephones.
简介:Earlyrepolarizationisawell-described,commonelectrocardiographicvariant.Itwasinitiallyfelttobebenign,butinthelasttwentyyearsasuggestedalinkbetweenspecifictypesofearlyrepolarizationandsuddencardiacdeathhasemerged.ThisassociationwashasbeentermedtheJwavesyndromeandincludesboththehighriskearlyrepolarizationandBrugadaECGpatterns.Theoddsofearlyrepolarizationchangebeingassociatedwithpooroutcomesarestillexceedinglysmall.Nevertheless,theassociationofafairlyubiquitousECGfindingwithfatalornearfatalclinicaloutcomeshasraisedconcern.Howcanweidentifythetrulyhigh-riskpatients?IfapatienthasasignificantclinicaleventwithaconcerningECGrepolarizationpattern,whatshouldbedonenext?TheauthorsofthisreviewpresentcurrentinformationregardingtheEarlyRepolarizationandBrugadaSyndromesandhowtoproceedwithdiagnosis,management,andriskstratificationwhenearlyrepolarizationchangeisobservedonECG.
简介:AUTOMATICCLASSIFICATIONOFECGUSINGARTIFICIALNEURALNETWORKSAUTOMATICCLASSIFICATIONOFECGUSINGARTIFICIALNEURALNETWORKSC.L.Peng,Z....
简介:摘要目的探讨血清中肿瘤标志物CEA、CA199、CA242、CA72.4、CA125在晚期胃癌中的临床应用价值。方法采用生化或放免分析法检测晚期胃癌患者血清CEA、CA199、CA242、CA72.4、CA125,与对照组比较并分析结果。结果32例胃癌患者及40例为胃良性患者及40例正常人CEA、CA199、CA242、CA72.4、CA125的敏感性依次为9.7.0%、5.6%、3.2%、6.2%、3.2%;晚期胃癌患者阳性率依次为37.5%、40.1%、28.1%、59.3%、18.7%。CA72.4的敏感性和特异性最高。结论CEA、CA199、CA242、CA72.4、CA125是晚期胃癌患者辅助诊断有价值的指标。
简介:Recentinterestinmobile-basedhealthcarehasdrivensignificantdemandsonresearchingnon-contactelectrodesforelectrocardiogram(ECG)measurement.Whiletheconductivegelachievestherequirementinmakingagoodcontactbetweentheelectrodesandskin,severalproblemsappear.Agel-free,non-contactelectrodebasedoncapacitivecouplingtheorywasprovidedinthispaper,whichwasintegratedontheprintcircuitboard(PCB).TheexperimentalresultsshowedthatclearECGsignalscouldbeacquiredinthelaboratoryconditionsbycouplingtheelectrodestothechestofpatientsthroughcottonbelts.
简介:模式经由确定的学习理论当模特儿和识别在这篇论文被介绍的为心电图(ECG)的一个方法。而不是认识到ECG表明beat-to-beat,包含很多心跳的每个ECG信号被认出。方法完全基于时间的特征(即,动力学)ECG模式,它包含ECG模式的完全的信息。一个动态模特儿被雇用表明方法,它能够产生合成ECG信号。基于动态模型,方法在下列二个阶段被显示出:鉴定(训练)阶段和识别(测试)分阶段执行。在鉴定阶段,ECG模式的动力学精确地被建模并且通过确定的学习表示了为经常的RBF神经重量。在识别阶段,当模特儿的结果被用于ECG模式识别。建议方法的主要特征是ECG模式的动力学精确地被当模特儿并且被用于ECG模式识别。用Physikalisch-TechnischeBundesanstalt(PTB)数据库的试验性的研究被包括表明途径的有效性。
简介:目的探讨胸水中CEA、CA125、CA153及CA199在肺癌诊断中的价值与意义。方法采用电化学发光免疫分析法测定40例肺癌患者与40例良性疾病患者胸水中的CEA、CA125、CA153及CA199水平,并对测定值进行组间比较;运用Logistic回归分析筛选出敏感指标,并进行联合检测。结果肺癌组患者胸水中的CEA、CA125、CA153及CA199水平分别为(43.28±8.19)ng/mL、(39.72±8.18)U/mL、(157.80±19.25)U/mL及(84.29±7.31)U/mL;良性疾病组四项标志物含量水平分别为(1.64±0.26)ng/mL、(26.47±4.26)U/mL、(12.31±3.86)U/mL及(31.47±6.10)U/mL,组间比较结果显示,肺癌组均高于良性疾病组中(P〈0.05)。以CEA、CA125、CA153及CA199为协变量,病理诊断结果作为应变量,CEA、CA125与CA153进入回归方程(P〈0.05)。肺癌组CEA、CA125、CA153单项检测阳性率分别为40.0%、52.5%与55.0%,三项联检阳性率为77.5%,显著高于各单项检测阳性率(P〈0.05)。结论胸水中CEA、CA125、CA153的检测在肺癌的诊断中具有重要意义,但是由于单项检测敏感性较低,可通过三项联合检测来克服单项指标的不足,提高肺癌诊断的敏感度及准确度。
简介:InviewoftheshortcomesofconventionalElectroCardioGram(ECG)compressionalgo-rithms,suchashighcomplexityofoperationanddistortionofreconstructedsignal,anewECGcompressionencodingalgorithmbasedonSetPartitioningInHierarchicalTrees(SPIHT)isbroughtoutafterstudyingtheintegerliftingschemewavelettransformindetail.Theproposedalgorithmmodifieszero-treestructureofSPIHT,establishessingledimensionalwaveletcoefficienttreeofECGsignalsandenhancestheefficiencyofSPIHT-encodingbydistributingbitsrationally,improvingzero-treesetandamelioratingclassifyingmethod.Forthisimprovedalgorithm,floating-pointcom-putationandstorageareleftoutofconsiderationanditiseasytobeimplementedbyhardwareandsoftware.Experimentalresultsprovethatthenewalgorithmhasadmirablefeaturesoflowcomplexity,highspeedandgoodperformanceinsignalreconstruction.Highcompressionratioisobtainedwithhighsignalfidelityaswell.