Cardiac Rhythm Management Research Department, University Hospital Southampton NHS Foundation Trust, Southampton, UK.
Faculty of Medicine, University of Southampton, Southampton, UK.
Ann Noninvasive Electrocardiol. 2023 Jul;28(4):e13056. doi: 10.1111/anec.13056. Epub 2023 Mar 15.
Machine learning methods are used in the classification of various cardiovascular diseases through ECG data analysis. The concept of varying subcutaneous implantable cardiac defibrillator (S-ICD) eligibility, owing to the dynamicity of ECG signals, has been introduced before. There are practical limitations to acquiring longer durations of ECG signals for S-ICD screening. This study explored the potential use of deep learning methods in S-ICD screening.
This was a retrospective study. A deep learning tool was used to provide descriptive analysis of the T:R ratios over 24 h recordings of S-ICD vectors. Spearman's rank correlation test was used to compare the results statistically to those of a "gold standard" S-ICD simulator.
A total of 14 patients (mean age: 63.7 ± 5.2 years, 71.4% male) were recruited and 28 vectors were analyzed. Mean T:R, standard deviation of T:R, and favorable ratio time (FVR)-a new concept introduced in this study-for all vectors combined were 0.21 ± 0.11, 0.08 ± 0.04, and 79 ± 30%, respectively. There were statistically significant strong correlations between the outcomes of our novel tool and the S-ICD simulator (p < .001).
Deep learning methods could provide a practical software solution to analyze data acquired for longer durations than current S-ICD screening practices. This could help select patients better suited for S-ICD therapy as well as guide vector selection in S-ICD eligible patients. Further work is needed before this could be translated into clinical practice.
通过心电图数据分析，机器学习方法被用于对各种心血管疾病进行分类。由于心电图信号的动态性，已经提出了改变皮下植入式心脏除颤器（S-ICD）适用性的概念。为 S-ICD 筛查获取更长时间的心电图信号存在实际限制。本研究探讨了深度学习方法在 S-ICD 筛查中的潜在应用。
这是一项回顾性研究。使用深度学习工具对 S-ICD 向量 24 小时记录的 T:R 比值进行描述性分析。使用 Spearman 秩相关检验对结果进行统计学比较，与 S-ICD 模拟器的“金标准”进行比较。
共招募了 14 名患者（平均年龄：63.7±5.2 岁，71.4%为男性），分析了 28 个向量。所有向量的平均 T:R、T:R 的标准差和本研究中引入的新概念——有利比时间（FVR）分别为 0.21±0.11、0.08±0.04 和 79±30%。我们的新工具的结果与 S-ICD 模拟器之间存在统计学上显著的强相关性（p<.001）。
深度学习方法可以为分析比当前 S-ICD 筛查实践更长时间的数据提供实用的软件解决方案。这有助于选择更适合 S-ICD 治疗的患者，并指导 S-ICD 适用患者的向量选择。在将其转化为临床实践之前，还需要进一步的工作。
