2022
DOI: 10.1161/circulationaha.121.058800
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Letter by Maqsood et al Regarding Article, “Anterior–Lateral Versus Anterior–Posterior Electrode Position for Cardioverting Atrial Fibrillation”

Abstract: al 1 performed a randomized, multicenter, unblinded study to address optimal electrode pad placement for the cardioversion of atrial fibrillation. In their conclusion, the authors claim that an anterior-lateral electrode configuration is superior to anterior-posterior (A-P) for elective cardioversion of atrial fibrillation.We believe that the study results may be misleading, specifically because it is likely that neither group had optimal electrode pad positioning. In the anterior-lateral group, which had rela… Show more

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Cited by 2 publications
(5 citation statements)
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“…In line with the comments by Ibrahim et al 1 and Maqsood et al, 2 we welcome future studies comparing other electrode positions under optimal cointerventions using contemporary biphasic maximum-fixed energy shocks.…”
Section: In Responsesupporting
confidence: 71%
See 2 more Smart Citations
“…In line with the comments by Ibrahim et al 1 and Maqsood et al, 2 we welcome future studies comparing other electrode positions under optimal cointerventions using contemporary biphasic maximum-fixed energy shocks.…”
Section: In Responsesupporting
confidence: 71%
“…We thank Ibrahim et al 1 and Maqsood et al 2 for the interest in our study, the EPIC (Electrode Position In Cardioverting Atrial Fibrillation) trial, 3 demonstrating anterior–lateral electrode positioning to be more effective than anterior–posterior electrode positioning for cardioverting atrial fibrillation.…”
Section: In Responsementioning
confidence: 94%
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“…Both models prioritize effective feature fusion for temporal learning and similarity assessment over multiple years. (Maqsood et al, 2022) TTCNN framework uses the energy layer (EL) to retain texture information, restrict the dimension of the final result vector, as well as enhance the algorithm's overall learning ability.…”
Section: References Descriptionmentioning
confidence: 99%
“…Adaptive Neuro Fuzzy Classifier (Rani et al, 2023) Due to its structure, which incorporates the intuitive nature of fuzzy logic systems with the learning capabilities of neural networks, this approach is employed for handling complex relationships in data. (Alkhaleefah et al, 2022;Abdelhafiz et al, 2020;Ashwini et al, 2023;Bai et al, 2022;Boudouh and Bouakkaz, 2023a;Lingampally and Kavuri, 2023;Liu et al, 2023;Mahmood et al, 2021Mahmood et al, , 2022Malathi and Latha, 2023;Maqsood et al, 2022;Cao et al, 2020;Castro-Tapia et al, 2021;Ertugrul and Abdullah, 2022;El Houby and Yassin, 2021;Falconi et al, 2020;George Melekoodappattu et al, 2022;Gerbasi et al, 2023;Gnanasekaran et al, 2020;Harris et al, 2023;Montaha et al, 2021;Huang and Lin, 2021;Jayandhi et al, 2022;Karthiga et al, 2022 (Cai et al, 2021;Narayanan et al, 2022;Nithya and Santhi, 2021;Oyelade and Ezugwu, 2022;Oza et al, 2022;Pawar et al, 2022;Ragab et al, 2021Ragab et al, , 2019Ravikumar et al, 2023;Kavitha et al, 2021) Histogram Equalization (Ahmad et al, 2023;Alfifi et al, 2020;Arora et al, 2020;Babu and Jerome, 2022;Baccouche et al, 2022;…”
Section: Continuation Ofmentioning
confidence: 99%