自动化方法的出现和命运,以区分广泛的QRS复杂性心跳动脉
Sarah LoCoco1, Anthony H Kashou2, Peter A Noseworthy2
1Department 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.
Journal of electrocardiology
|July 30, 2023
概括
使用心电图 (ECG) 解释软件的自动化方法可以准确地区分广泛复杂的心跳动 (WCT). 本综述分析了当前的机器学习方法,以区分心室低心率 (VT) 和心室以上宽复杂高心率 (SWCT).
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 准确地将宽复杂性心力衰竭 (WCTs) 分别为心室性心力衰竭 (VT) 或脑上宽复杂性心力衰竭 (SWCT) 对于患者的管理至关重要.
- 非侵入性12导电心电图 (ECG) 解释是主要的诊断工具,但准确性可能具有挑战性.
- 使用计算机化心电图解释软件的自动化方法显示出改善WCT差异化的前景.
研究的目的:
- 提供对当代自动化方法的全面审查,以区分VT和SWCT.
- 检查机器学习 (ML) 技术在自动化WCT差异化中的作用.
- 讨论这些算法和未来的集成策略的基础上的电生理学概念.
主要方法:
- 关于自动化WCT差异化的当代文献的审查.
- 对机器学习技术的分析,应用于用于WCT分类的ECG解释.
- 检查现有算法中使用的电生理学原理.
主要成果:
- 新兴的自动化方法表明,对于精确的WCT差异化有很大的潜力.
- 机器学习算法越来越多地被使用,利用复杂的心电图特征.
- 目前的解决方案看起来很有前途,但将其集成到临床平台中需要进一步开发.
结论:
- 自动化WCT差异化具有显著的临床潜力,提高了诊断准确度.
- 继续在ML和电生理学方面的研究对于完善这些工具至关重要.
- 成功集成到计算机化心电图解释平台将改善患者的护理.
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