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Background: 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. Methods: 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. Results: 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). Conclusion: 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. [ABSTRACT FROM AUTHOR]

Abstract Background 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. Methods 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. Results 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