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.

(2023). Correlation analysis of deep learning methods in S-ICD screening [journal article - articolo]. In ANNALS OF NONINVASIVE ELECTROCARDIOLOGY. Retrieved from https://hdl.handle.net/10446/245109

Correlation analysis of deep learning methods in S-ICD screening

Coniglio, Stefano;
2023-03-15

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 < .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.
articolo
15-mar-2023
Elrefai, Mohamed; Abouelasaad, Mohamed; Wiles, Benedict M.; Dunn, Anthony J.; Coniglio, Stefano; Zemkoho, Alain B.; Morgan, John; Roberts, Paul R....espandi
(2023). Correlation analysis of deep learning methods in S-ICD screening [journal article - articolo]. In ANNALS OF NONINVASIVE ELECTROCARDIOLOGY. Retrieved from https://hdl.handle.net/10446/245109
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/245109
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