| Autor: |
Mohamed ElRefai, Mohamed Abouelasaad, Isobel Conibear, Benedict M. Wiles, Anthony J. Dunn, Stefano Coniglio, Alain B. Zemkoho, John Morgan, Paul R. Roberts |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Indian Pacing and Electrophysiology Journal, Vol 24, Iss 4, Pp 192-199 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
0972-6292 |
| DOI: |
10.1016/j.ipej.2024.06.003 |
| Popis: |
Introduction: The risk of complications associated with transvenous ICDs make the subcutaneous implantable cardiac defibrillator (S-ICD) a valuable alternative in patients with adult congenital heart disease (ACHD). However, higher S-ICD ineligibility and higher inappropriate shock rates-mostly caused by T wave oversensing (TWO)- are observed in this population. We report a novel application of deep learning methods to screen patients for S-ICD eligibility over a longer period than conventional screening. Methods: Adult patients with ACHD and a control group of normal subjects were fitted with a 24-h Holters to record their S-ICD vectors. Their T:R ratio was analysed utilising phase space reconstruction matrices and a deep learning-based model to provide an in-depth description of the T: R variation plot for each vector. T: R variation was compared statistically using t-test. Results: 13 patients (age 37.4 ± 7.89 years, 61.5 % male, 6 ACHD and 7 control subjects) were enrolled. A significant difference was observed in the mean and median T: R values between the two groups (p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
