Automatic supporting system for regionalization of ventricular tachycardia exit site in implantable defibrillators

Electrograms stored in Implantable Cardioverter Defibrillators (ICD-EGM) have been proven to convey useful information for roughly determining the anatomical location of the Left Ventricular Tachycardia exit site (LVTES). Our aim here was to evaluate the possibilities from a machine learning system intended to provide an estimation of the LVTES anatomical region with the use of ICD-EGM in the situation where 12-lead electrocardiogram of ventricular tachycardia are not available. Several machine learning techniques were specifically designed and benchmarked, both from classification (such as Neural Networks (NN), and Support Vector Machines (SVM)) and regression (Kernel Ridge Regression) problem statements. Classifiers were evaluated by using accuracy rates for LVTES identification in a controlled number of anatomical regions, and the regression approach quality was studied in terms of the spatial resolution. We analyzed the ICD-EGM of 23 patients (18±10 EGM per patient) during left vent...

存储于植入式心律转复除颤器（Implantable Cardioverter Defibrillators, ICD）中的电图（ICD-EGM）已被证实可提供有效信息，用于粗略推断左心室心动过速出口位点（Left Ventricular Tachycardia exit site, LVTES）的解剖位置。本研究旨在评估一款机器学习系统的应用潜力：当无法获取心室心动过速的12导联心电图时，该系统可基于ICD-EGM对LVTES的解剖区域进行估算。我们专门设计并基准测试了多种机器学习技术，覆盖分类任务（如神经网络（Neural Networks, NN）、支持向量机（Support Vector Machines, SVM））与回归任务（核岭回归（Kernel Ridge Regression））。分类模型的性能通过受控数量解剖区域内的LVTES识别准确率进行评估，回归方法的质量则通过空间分辨率加以衡量。我们分析了23例患者的ICD-EGM数据（每位患者平均18±10份电图），相关分析针对左心室……