人工智能增强了对年轻人年龄和性别分类的心电图分析
Honggen Zhang1, Mohammad Zaeri-Amirani1, Mojtaba Abolfazli1
1University of Hawaii.
Research square
|November 24, 2025
概括
机器学习模型从儿童和青少年的心电图 (ECG) 数据准确预测年龄和性别. 这有助于制定新的儿科心电图标准,以改善临床分析.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 电心电图 (ECG) 值显示出显著的年龄和性别差异,特别是在儿科患者群体中.
- 现有的特定于年龄和性别的ECG标准可能无法完全捕捉复杂的关系,并且在机器学习 (ML) 应用中未得到充分利用.
- 使用ML的自动心电图分析提高了临床准确性,但儿科研究很少.
研究的目的:
- 使用机器学习 (ML) 建模,为儿科心电图 (ECG) 制定特定年龄和性别的标准.
- 提高儿童和青少年自动化心电图分析的准确性.
- 调查ML在建立新的儿科心电图参考范围中的实用性.
主要方法:
- 分析了29408个精心策划的休息12心电图,这些心电图来自0-21岁的健康个体.
- 利用了177个数字化的心电图变量与各种ML模型,包括回归,分类和半监督的神经网络.
- 在重复的火车测试分割中使用F1分数,AUROC和混矩阵评估模型性能.
主要成果:
- 支持矢量机 (SVM) 在建模年龄和性别方面表现出最高的准确性.
- 主要的预测性心电图特征包括心率,PR间隔,QRS持续时间和T波幅度.
- 在青少年中,SVM在年龄组分类方面达到94%的准确性 (允许错误分类) 和高F1得分 (0.91) 和AUROC (0.95) 在性别分类方面.
结论:
- 监督ML模型有效捕捉与年龄和性别相关的生理心电图变化,优于半监督方法,特别是在较小的子组中.
- 这些发现支持为儿科心脏病研究和临床实践创建特定于年龄和性别的ML增强的ECG标准.
- 这项研究突出了ML在细化儿科心电图解释诊断标准方面的潜力.
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