AttBiLFNet:一种新的混合网络,用于在不平衡的心电图信号中准确有效地检测心律失常
1Department of Electrical and Electronics Engineering, Hitit University, Corum 19030, Turkey.
Mathematical biosciences and engineering : MBE
|June 14, 2024
概括
一个新的混合模型,AttBiLFNet,可以从心电图 (ECG) 信号中准确检测心律失常,即使数据不平衡. 这一进步为及时识别心律异常提供了可靠的工具.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
背景情况:
- 心律失常是心脏突然死亡的主要原因,可通过心电图 (ECG) 检测到.
- 传统的心电图分析在主观性和监测持续时间方面存在局限性.
- 电脑心电图数据中的类不平衡对准确检测心律失常提出了重大挑战.
研究的目的:
- 提出一种新的混合模型,AttBiLFNet,用于精确检测心律失常的心电图信号.
- 为了应对心电图数据集中不平衡的类分布所带来的挑战.
- 为了提高自动心律失常识别的准确性和效率.
主要方法:
- 开发了AttBiLFNet,这是一个混合模型,集成双向长期短期记忆 (BiLSTM) 和卷积神经网络 (CNN).
- 整合了注意力机制,以专注于相关的ECG信号段.
- 利用焦点损失功能有效地管理类不平衡问题.
主要成果:
- AttBiLFNet实现了高性能指标,包括99.55%的准确性和98.52%的精度.
- 该模型在准确性和计算效率方面,与现有方法相比,表现优越.
- 计算了MF1评分,K评分和灵敏度等关键指标,证实了模型的稳定性.
结论:
- AttBiLFNet提供了一种可靠和高效的解决方案,用于自动检测ECG信号中的心律失常.
- 该模型能够处理阶级不平衡,使其适合于现实世界的临床应用.
- 这项研究有助于改善及时识别可能危及生命的心律失常症.
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