The emergence and destiny of automated methods to differentiate wide QRS complex tachycardias.

Autor: LoCoco S; Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, 660 S. Euclid Ave, CB 8086, St. Louis, MO 63110, United States of America. Electronic address: lococosarah@wustl.edu., Kashou AH; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America., Noseworthy PA; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America., Cooper DH; Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, 660 S. Euclid Ave, CB 8086, St. Louis, MO 63110, United States of America., Ghadban R; Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, 660 S. Euclid Ave, CB 8086, St. Louis, MO 63110, United States of America., May AM; Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, 660 S. Euclid Ave, CB 8086, St. Louis, MO 63110, United States of America.
Jazyk: angličtina
Zdroj: Journal of electrocardiology [J Electrocardiol] 2023 Nov-Dec; Vol. 81, pp. 44-50. Date of Electronic Publication: 2023 Jul 23.
DOI: 10.1016/j.jelectrocard.2023.07.008
Abstrakt: Accurate differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using non-invasive methods such as 12‑lead electrocardiogram (ECG) interpretation is crucial in clinical practice. Recent studies have demonstrated the potential for automated approaches utilizing computerized ECG interpretation software to achieve accurate WCT differentiation. In this review, we provide a comprehensive analysis of contemporary automated methods for VT and SWCT differentiation. Our objectives include: (i) presenting a general overview of the emergence of automated WCT differentiation methods, (ii) examining the role of machine learning techniques in automated WCT differentiation, (iii) reviewing the electrophysiology concepts leveraged existing automated algorithms, (iv) discussing recently developed automated WCT differentiation solutions, and (v) considering future directions that will enable the successful integration of automated methods into computerized ECG interpretation platforms.
(Copyright © 2023 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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