机器学习算法的比较使用手动/自动功能对12导信号心电图的分类:对年龄在6至18岁之间的学生进行的一项大型队列研究
Ghasem Hajianfar1, Mohammadrafie Khorgami2, Yousef Rezaei1,3
1Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Niayesh Highway, Valiasr Ave., Tehran, 19969111541, Iran.
Cardiovascular engineering and technology
|October 17, 2023
概括
儿童手动心电图 (ECG) 测量显示,比自动化检测节律障碍的方法更准确. 机器学习分类器在区分异常心电图方面也显示出有希望的结果.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 电心电图 (ECG) 对于识别心律障碍至关重要.
- 机器学习 (ML) 为自动化心电图分析提供了潜力.
研究的目的:
- 评估ML算法在区分正常儿科心电图和异常儿科心电图方面的准确性.
- 为了将手动ECG特征测量与使用MEANS算法自动测量进行比较.
主要方法:
- 分析了来自6-18岁学生的10,745个休息的12-Lead心电图.
- 手动和自动心电图特征被提取,规范化,并测试相关性.
- 在使用Boruta算法选择特征后,应用了8个ML分类器.
主要成果:
- 手动心电图测量在准确性方面超过了自动化 (MEANS) 测量.
- 随机森林和QDA分类器在手动数据上实现了高性能 (AUC高达0.93).
- 在自动化数据上,QDA和堆学习显示出强的结果 (AUC高达0.89).
结论:
- 手动心电图特征分析表明,与自动化MEANS算法相比,其性能优越.
- 某些ML分类器显示出有效检测儿童心电图异常的潜力.
- 需要进一步的研究来评估ML在社区ECG查中的有效性.
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