IRIDIA-AF,一个长期心电图监测大发性心房的数据库
Cédric Gilon1, Jean-Marie Grégoire2,3, Marianne Mathieu2
1IRIDIA, Université libre de Bruxelles, Brussels, Belgium. cedric.gilon@ulb.be.
Scientific data
|October 18, 2023
概括
现在可以使用一种新的霍尔特监测数据集来检测心房动 (AF). 该资源有助于研究人员开发机器学习和深度学习工具,用于诊断心律不整.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 心房动 (AF) 是一种普遍的心律失常症,需要准确的诊断.
- 霍尔特监测提供了长期心电图 (ECG) 数据,对于检测AF至关重要.
- 机器学习 (ML) 和深度学习 (DL) 在自动化从心电图数据中检测AF方面表现有前途.
研究的目的:
- 引入一种新的,注释的霍尔特监测数据集,用于 Paroxysmal AF.
- 为培训和验证心脏病学ML/DL模型扩大高质量的数据的可用性.
- 为了促进自动心律失常检测的进步.
主要方法:
- 汇编了152名患有偏发性AF的患者的167个霍尔特监测记录.
- 由心脏病专家和心脏护士对AF发作进行手动注释和专家审查.
- 数据集包括2400万秒的数据,采用200赫兹的数据样本,记录时间从19到95小时不等.
主要成果:
- 这里介绍了一个关于 Paroxysmal AF 的 Holter 监测数据的综合数据集.
- 该数据集包括AF情节的详细注释,由临床专家审查.
- 数据跨越2400万秒,为研究提供了大量的数据.
结论:
- 新发布的霍尔特监测数据集是研究界的宝贵资源.
- 它支持用于AF诊断的先进ML/DL算法的开发和验证.
- 这有助于改善临床心脏病学中的决策支持工具,用于检测心律失常.
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