评估使用卷积神经网络的长RR间隔在单线长期霍尔特心电图记录的卷积神经网络
Tan Lyu1, Miao Ye1, Minjie Yuan1
1Electrocardiogram Department, Sir Run Run Shaw Hospital, Affiliated with the Zhejiang University School of Medicine, HangZhou, 310016, Zhejiang, China.
Scientific reports
|April 8, 2025
概括
卷积神经网络 (CNN) 在长期心电图监测中有效检测延长的RR间隔 (RRI). 这种人工智能方法提高了诊断效率和医生对大数据集的信心.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
背景情况:
- 延长的心电图监测 (7-14天) 产生了大量的数据,挑战了医生.
- 使用人工智能的自动心律失常检测显示出希望,但需要临床验证.
- 缺乏对长时间RR间隔 (RRI) 检测算法的系统评估.
研究的目的:
- 综合评估卷积神经网络 (CNN) 算法用于检测延长的RR间隔 (RRI).
- 评估AI在分析RRI的长期ECG数据中的临床实用性.
- 为部署人工智能驱动的ECG监测设备提供可靠的基础.
主要方法:
- 从长期RRI患者中收集了480个单一的长期动态心电图记录.
- 使用CNN算法和手动分析 (黄金标准) 分析了ECG数据.
- 根据回忆,精度和F1分数来评估CNN算法性能.
主要成果:
- CNN算法实现了高性能指标:98.41%的回忆,98.68%的精度和98.54%的F1得分.
- 证明了CNN能够满足长期RRI识别的临床需求的能力.
- 表明AI对大型心电图数据集的诊断效率有了显著的改善.
结论:
- 在长期心电图监测中,CNN算法在临床上有效地识别长期RRI.
- 人工智能显著提高了医生在分析大量心电图数据时的信心和诊断效率.
- 这些发现支持在各种医疗保健机构中部署AI支持的单线电图监测.
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