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Deep learning methods for screening patients' S-ICD implantation eligibility
Artificial Intelligence in Medicine ( IF 7.5 ) Pub Date : 2021-08-09 , DOI: 10.1016/j.artmed.2021.102139
Anthony J Dunn 1 , Mohamed H ElRefai 2 , Paul R Roberts 2 , Stefano Coniglio 1 , Benedict M Wiles 3 , Alain B Zemkoho 1
Affiliation  

Subcutaneous Implantable Cardioverter-Defibrillators (S-ICDs) are used for prevention of sudden cardiac death triggered by ventricular arrhythmias. T Wave Over Sensing (TWOS) is an inherent risk with S-ICDs which can lead to inappropriate shocks. A major predictor of TWOS is a high T:R ratio (the ratio between the amplitudes of the T and R waves). Currently, patients' Electrocardiograms (ECGs) are screened over 10 s to measure the T:R ratio to determine the patients' eligibility for S-ICD implantation. Due to temporal variations in the T:R ratio, 10 s is not a long enough window to reliably determine the normal values of a patient's T:R ratio. In this paper, we develop a convolutional neural network (CNN) based model utilising phase space reconstruction matrices to predict T:R ratios from 10-second ECG segments without explicitly locating the R or T waves, thus avoiding the issue of TWOS. This tool can be used to automatically screen patients over a much longer period and provide an in-depth description of the behavior of the T:R ratio over that period. The tool can also enable much more reliable and descriptive screenings to better assess patients' eligibility for S-ICD implantation.



中文翻译:

筛选患者 S-ICD 植入资格的深度学习方法

皮下植入式心脏复律除颤器 (S-ICD) 用于预防由室性心律失常引发的心源性猝死。T 波过感应 (TWOS) 是 S-ICD 的固有风险,可导致不适当的电击。TWOS 的一个主要预测因素是高 T:R 比(T 波和 R 波振幅之间的比值)。目前,患者的心电图 (ECG) 筛查时间超过 10 秒,以测量 T:R 比,以确定患者是否适合 S-ICD 植入。由于 T:R 比率的时间变化,10 s 不是一个足够长的窗口,无法可靠地确定患者 T:R 比率的正常值。在本文中,我们开发了一个基于卷积神经网络 (CNN) 的模型,利用相空间重构矩阵来预测 T:来自 10 秒心电图段的 R 比率,无需明确定位 R 或 T 波,从而避免了 TWOS 问题。该工具可用于在更长的时间内自动筛选患者,并提供该期间 T:R 比率行为的深入描述。该工具还可以实现更可靠和更具描述性的筛查,以更好地评估患者是否适合 S-ICD 植入。

更新日期:2021-08-20
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