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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：BackgroundThoracoscopicminimallyinvasivepectusexcavatumrepair(Nussoperation)featuresitslittletrauma,simple,shortoperationtime,andgoodoutcomecomparedwithtraditionaltreatmentofpectusexcavatumsurgery-sternalelevation(Ravitchoperation)andsternalturnover.Theeffectoftheoperationonpatients’heartandheartfunctionremainsunclear.ThisstudyaimedtounderstandthechangesofelectrocardiogramandcardiacfunctionafterNussprocedure.MethodsFrom2008Januaryto2013July,thoracoscopicNussoperationwasperformedin217patientswithpectusexcavatum.Allthepatientsunderwentthepreoperative,postoperativedetectionofECGandcardiacfunctionin3monthsto1yearafteroperation.ResultsAfter3monthsto1yearfollow-up,arrhythmiaspersistedin46outof135patientswithpreoperativesymptoms(P<0.05);Strokevolumeandcardiacoutputsignificantlyincreased(P<0.05);Andcardiacparametersgreatlyimproved(P<0.05).ConclusionsMinimallyinvasiverepairofpectusexcavatumdeformitycancorrectthechestmalformation,alleviatearrhythmia,andimprovecardiacfunction.

简介：AbstractBackground:A deep learning model (DLM) that enables non-invasive hypokalemia screening from an electrocardiogram (ECG) may improve the detection of this life-threatening condition. This study aimed to develop and evaluate the performance of a DLM for the detection of hypokalemia from the ECGs of emergency patients.Methods:We used a total of 9908 ECG data from emergency patients who were admitted at the Second Affiliated Hospital of Nanchang University, Jiangxi, China, from September 2017 to October 2020. The DLM was trained using 12 ECG leads (lead I, II, III, aVR, aVL, aVF, and V1-6) to detect patients with serum potassium concentrations <3.5 mmol/L and was validated using retrospective data from the Jiangling branch of the Second Affiliated Hospital of Nanchang University. The blood draw was completed within 10 min before and after the ECG examination, and there was no new or ongoing infusion during this period.Results:We used 6904 ECGs and 1726 ECGs as development and internal validation data sets, respectively. In addition, 1278 ECGs from the Jiangling branch of the Second Affiliated Hospital of Nanchang University were used as external validation data sets. Using 12 ECG leads (leads I, II, III, aVR, aVL, aVF, and V1-6), the area under the receiver operating characteristic curve (AUC) of the DLM was 0.80 (95% confidence interval [CI]: 0.77-0.82) for the internal validation data set. Using an optimal operating point yielded a sensitivity of 71.4% and a specificity of 77.1%. Using the same 12 ECG leads, the external validation data set resulted in an AUC for the DLM of 0.77 (95% CI: 0.75-0.79). Using an optimal operating point yielded a sensitivity of 70.0% and a specificity of 69.1%.Conclusions:In this study, using 12 ECG leads, a DLM detected hypokalemia in emergency patients with an AUC of 0.77 to 0.80. Artificial intelligence could be used to analyze an ECG to quickly screen for hypokalemia.