University of Southampton, School of Mathematical Sciences, United Kingdom.
University Hospital of Southampton, United Kingdom.
Artif Intell Med. 2021 Sep;119:102139. doi: 10.1016/j.artmed.2021.102139. Epub 2021 Aug 9.
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.
皮下植入式心律转复除颤器（S-ICD）用于预防由室性心律失常引发的心脏性猝死。T 波超感（TWOS）是 S-ICD 的固有风险，可能导致不适当的电击。TWOS 的一个主要预测因素是 T:R 比值（T 波和 R 波幅度之间的比值）高。目前，患者的心电图（ECG）经过 10 秒的筛选，以测量 T:R 比值，以确定患者是否适合植入 S-ICD。由于 T:R 比值存在时间变化，10 秒的时间窗口不足以可靠地确定患者 T:R 比值的正常值。在本文中，我们开发了一种基于卷积神经网络（CNN）的模型，利用相空间重建矩阵从 10 秒 ECG 片段预测 T:R 比值，而无需明确定位 R 波或 T 波，从而避免了 TWOS 问题。该工具可用于对患者进行更长时间的自动筛选，并提供该时间段内 T:R 比值行为的详细描述。该工具还可以进行更可靠和描述性的筛选，以更好地评估患者是否适合植入 S-ICD。
