使用深度学习在正常鼻节律期间使用单导移动心电图识别心房动
Jiwoong Kim1,2, Sun Jung Lee3, Bonggyun Ko1,4
1Department of Mathematics and Statistics, Chonnam National University, Gwangju, Korea.
Journal of Korean medical science
|February 6, 2024
概括
这项研究表明,深度学习模型可以在正常鼻节律 (NSR) 期间使用单导移动心电图 (ECG) 检测心房动 (AF). ResNet50表现出最好的性能,突出了远程心律失常查的潜力.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗技术 医疗技术 医学技术
背景情况:
- 移动设备提供实用的单导电心电图 (ECG) 采集,用于检测心律失常.
- 人工智能 (AI) 提高了心房动 (AF) 查效率.
- 在正常鼻节律 (NSR) 期间使用单线心电图识别AF需要进一步探索.
研究的目的:
- 引入和评估一种在NSR期间使用单导移动心电图识别AF的方法.
- 评估深度学习模型在从NSR心电图数据中检测AF的有效性.
主要方法:
- 使用了三个深度学习模型 (RNN,LSTM,ResNet50).
- 过了10,287个NSR心电图的数据集,将其细分为10秒间隔,并进行了预处理.
- 进行了31,767个细分段 (15,157个蒙面AF,16,610个健康) 的随机不足样本和分析.
主要成果:
- ResNet50获得了最高的性能:79.3%的回忆率,65.8%的精度,71.9%的F1得分,70.5%的准确率和0.79的AF检测AUC.
- RNN和LSTM模型的比较性能分数分别为0.75和0.74.
- 外部验证表明ResNet50的F1得分为64.1%,回忆率为68.9%,精度为60.0%,准确率为63.4%,AUC为0.68.
结论:
- 在NSR期间使用单线移动心电图的深度学习模型可以有效地识别面临AF风险的个体.
- 进一步的研究是必要的,以提高模型的性能,以广泛的临床应用.
相关概念视频
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