从使用深度学习的24小时霍尔特记录中对患有心房的患者进行自动查
Peng Zhang1,2, Fan Lin3, Fei Ma3
1Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, China.
European heart journal. Digital health
|June 2, 2023
概括
一个新的深度学习算法可以使用24小时的霍尔特显示器数据自动选心房动 (AF). 这种准确而有效的工具有助于AF的初级查,解决了经验丰富的心脏病专家的短缺问题.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 对心房动 (AF) 查的需求日益增加,需要有效的数据分析.
- 从长期心电图监测中手动识别AF是耗时且主观的.
- 经验丰富的心脏病专家的短缺限制了广泛的AF查.
研究的目的:
- 开发和验证用于自动化AF查的深度学习算法.
- 为了利用24小时的霍尔特监控数据进行初级AF检测.
- 为识别AF患者提供可扩展的解决方案.
主要方法:
- 开发了一个深度学习模型,使用RR间隔从24小时的霍尔特录音.
- 该模型在一个包含23,621条记录的大数据集上进行了训练和评估.
- 在独立的医院和社区测试集以及外部数据集上进行了绩效评估.
主要成果:
- 该算法在测试组中识别AF患者时取得了高准确性 (例如,灵敏度0.995,特异性0.985).
- 在外部公开数据集上观察到一致的性能 (灵敏度为1.000,特异性为0.972).
- 该模型根据6分钟或更长时间的发作确定了AF.
结论:
- 一个深度学习模型可以完全自动化从霍尔特数据的初级AF选,以高准确度.
- 这种自动化方法为AF查提供了强大且具有成本效益的工具.
- 该算法解决了手动解释和心脏病学家可用性的局限性.
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