心律失常导致去极化波在心肌表面传导不均匀,当以较快的频率刺激时,可能会相对于先前的节拍延迟局部分量。尽管定位不同的局部心电图(EGM)分量对识别具有衰减诱发电位(DEEP)的区域具有诊断价值,当前的软件解决方案不执行自动信号量化。电生理学家必须手动测量EGM信号上的距离,以评估起搏或额外刺激方案期间DEEP的存在。在这项工作中,我们提出了一种基于深度学习(DL)的算法,用于从EGM信号中识别心房分量(在冠状窦中测量)相对于心室分量的衰减,用于区分辅助途径(AP)和房室折返性心动过速(AVRTs)。在冠状窦信号的私有数据集上训练了具有不同配置的几个U-Net和W-Net神经网络(来自77例接受AP或AVRT消融的患者的312个EGM记录)。一秒,单独的数据集被注释用于临床验证,与EGM片段相关的临床标记,其中阐明了传导衰减。为了缓解数据稀缺,开发了一种合成数据增强方法来生成EGM记录。此外,开发了两个新颖的损失函数,以最大程度地减少假阴性和轮廓错误。最后,探讨了自我注意力机制的加入及其对模型性能的影响。性能最好的模型是具有6个级别的W-Net模型,仅与骰子损失优化。该模型获得了91.28%的精度,77.78%和100.0%,召回率达到94.86%,局部场定位95.25%和100.0%,远场激活,和额外的刺激,分别。临床验证模型在评估衰减特性方面表现出良好的总体一致性。与电生理学家的标准相比,自动排除步骤的敏感性为87.06%,特异性为97.03%。在非排除信号中,将它们分为递减和非递减电位的敏感性为96.77%,特异性为95.24%。当前的结果显示出巨大的希望,据我们所知,文献中的第一个工具,允许描述EGM记录中存在的所有局部组件。这对于推进心脏电生理程序的处理和减少干预时间非常重要。因为许多诊断程序是通过比较随后的心动周期中的节段或晚期电位来执行的。
Cardiac arrhythmias cause depolarization waves to conduct unevenly on the myocardial surface, potentially delaying local components with respect to a previous beat when stimulated at faster frequencies. Despite the diagnostic value of localizing the distinct local electrocardiogram (EGM) components for identifying regions with decrement-evoked potentials (DEEPs), current software solutions do not perform automatic signal quantification. Electrophysiologists must manually measure distances on the EGM signals to assess the existence of DEEPs during pacing or extra-stimuli protocols. In this work, we present a deep learning (DL)-based algorithm to identify decrement in atrial components (measured in the coronary sinus) with respect to their ventricular counterparts from EGM signals, for disambiguating between accessory pathways (APs) and atrioventricular re-entrant tachycardias (AVRTs). Several U-Net and W-Net neural networks with different configurations were trained on a private dataset of signals from the coronary sinus (312 EGM recordings from 77 patients who underwent AP or AVRT ablation). A second, separate dataset was annotated for clinical validation, with clinical labels associated to EGM fragments in which decremental conduction was elucidated. To alleviate data scarcity, a synthetic data augmentation method was developed for generating EGM recordings. Moreover, two novel loss functions were developed to minimize false negatives and delineation errors. Finally, the addition of self-attention mechanisms and their effect on model performance was explored. The best performing model was a W-Net model with 6 levels, optimized solely with the Dice loss. The model obtained precisions of 91.28%, 77.78% and of 100.0%, and recalls of 94.86%, 95.25% and 100.0% for localizing local field, far field activations, and extra-stimuli, respectively. The clinical validation model demonstrated good overall agreement with respect to the evaluation of decremental properties. When compared to the criteria of electrophysiologists, the automatic exclusion step reached a sensitivity of 87.06% and a specificity of 97.03%. Out of the non-excluded signals, a sensitivity of 96.77% and a specificity of 95.24% was obtained for classifying them into decremental and non-decremental potentials. Current results show great promise while being, to the best of our knowledge, the first tool in the literature allowing the delineation of all local components present in an EGM recording. This is of capital importance at advancing processing for cardiac electrophysiological procedures and reducing intervention times, as many diagnosis procedures are performed by comparing segments or late potentials in subsequent cardiac cycles.