基于心电图的多类心律失常的分类使用节拍级融合网络
Junyuan Jing1, Jing Zhang1, Aiping Liu1
1School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China.
Journal of healthcare engineering
|December 11, 2023
概括
一个新的节拍水平融合网络 (BLF-Net) 通过对个体心跳进行加权来改善心律失常的分类. 这种深度学习方法可以通过心电图 (ECG) 数据来加强心血管疾病的诊断.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 心血管疾病 (CVD) 构成了全球严重的健康威胁.
- 电心电图 (ECG) 是心血管疾病的关键诊断工具,特别是用于检测心律失常.
- 深度学习方法,特别是那些结合注意力机制的方法,在心电图分析中表现有前途,但往往侧重于时间点加权.
研究的目的:
- 引入一种新的深度学习模型,即Beat-Level Fusion Net (BLF-Net),用于多类心律失常的分类.
- 通过将注意力机制应用于心跳水平而不是仅仅是时间点来解决现有方法的局限性.
- 从心电图信号提高自动心律失常检测的准确性和可解释性.
主要方法:
- 拟议的BLF-Net将长时间的ECG信号分割成个别的心跳.
- 一个神经网络从每个心跳中提取特征.
- 一个注意力机制根据它们的诊断贡献将权重分配给心跳特征.
主要成果:
- 与最先进的方法相比,BLF-Net在使用PTB-XL数据库的六个分类任务中表现出更高的性能.
- 对注意力机制权重的可视化提供了对模型决策过程的见解.
- 该模型在多类心律失常的分类中取得了很高的准确性.
结论:
- BLF-Net提供了一种有效和自动化的方法来对心律失常进行分类.
- 关注心跳水平可以提高心电图分析的诊断能力.
- 这种方法有可能显著帮助心脏病专家诊断心律不整.
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