SHDB-AF:一个关于心房的日本霍尔特心电图数据库
Kenta Tsutsui1,2, Shany Biton Brimer3, Noam Ben-Moshe3,4
1Division of Cardiology, Department of Internal Medicine, Teikyo University School of Medicine, Tokyo, Japan. knt22e@gmail.com.
Scientific data
|March 20, 2025
概括
一个新的日本心房动 (AF) ECG数据集,SHDB-AF,现在可用了. 这种开源的Holter ECG数据库支持强大的深度学习模型开发,以改善心律失常的诊断.
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:
- 心房动 (AF) 是一种普遍的心律失常,与中风和心力衰竭等重大健康问题有关.
- 机器学习 (ML) 和深度学习 (DL) 显示出改善AF诊断的希望.
- 现有的心电图数据库缺乏来自日本人口的代表性,这阻碍了模型的通用性.
研究的目的:
- 来自日本的Saitama心脏数据库心房动 (SHDB-AF),一个新的霍尔特心电图数据集.
- 为开发用于AF检测的强大和可通用DL模型提供资源.
- 为了弥补有关日本种族的ECG数据多样性的差距.
主要方法:
- 该SHDB-AF数据集包括来自122名日本患者的128个霍尔特心电图记录.
- 每个记录长达24小时,有两个心电图通道,共计2160万秒的数据.
- 该数据集包括每个患者的详细临床信息.
主要成果:
- 现在,SHDB-AF数据集在PhysioNet.net上公开提供.
- 该资源使研究人员能够在各种数据集上训练和验证DL算法.
- 方便在以前没有充分代表的特定人口群体中研究AF.
结论:
- SHDB-AF数据集是对心律失常研究领域的宝贵贡献.
- 这些日本心电图数据的可用性将有助于开发更公平的AI诊断工具.
- 鼓励进一步研究人口特异性AF变异及其检测.
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