将心电图转换为参与者级诊断在心房的查中
概括
一种新的动态训练方法改善了单线心电图的心房动 (AF) 检测,在真实世界的应用中在心电图分类和参与者诊断方面都超过了固定的方法.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 医疗保健中的机器学习
背景情况:
- 单导电心电图 (ECG) 在临床环境之外越来越多地被捕获,使得大脑动 (AF) 的广泛查成为可能.
- 挑战包括低的AF流行率和每个人的众多心电图,使得准确的参与者诊断变得复杂.
- 有效的AF检测模型对于早期干预和中风预防至关重要.
研究的目的:
- 引入和评估使用单线心电图的AF检测模型的动态培训方法.
- 为了比较动态方法与固定的培训策略的性能.
- 评估两种方法在ECG级分类和参与者级诊断方面的有效性.
主要方法:
- 一个基于RR间隔的AF检测模型使用Screening for Atrial Fibrillation with ECG to Reduce Stroke (SAFER) 数据集进行了训练.
- 实行了动态训练方法,在模型训练中结合了更多的非AF ECG,而不是固定方法.
- 参与者级别的测试是通过应用训练的ECG分类模型来进行的.
主要成果:
- 动态培训策略在所有指标上始终优于固定策略.
- 动态方法实现了较高的接收器运行特征曲线 (AUROC) 下面面积得分 (0.92-0.94对比0.90-0.92).
- 动态方法在心电图和参与者水平上产生了优异的精度回忆曲线下的区域 (AUPRC) 评分 (0.38-0.75对比0.12-0.66).
结论:
- 动态训练方法显著提高了单线心电图的AF检测的准确性.
- 这种方法在参与者级别的诊断上表现强,这对于临床实用性至关重要.
- 结果支持动态方法作为现实世界AF查和管理的关键进步.
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