基于具有注意力机制的多输入卷积神经网络的心律失常分类
Bin Zheng1, Wenbo Luo1, Mingming Zhang1,2
1School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing, China.
PloS one
|June 17, 2025
概括
这项研究引入了一种新的深度学习模型,用于使用多尺度心电图分析进行心律失常的分类. 先进的算法实现了高精度,改善了心律失常的检测.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 心脏病学 心脏病学
背景情况:
- 心律失常会带来严重的健康风险,包括中风和心脏骤停.
- 深度学习已经改善了心电图分析,但信号变化和数据不平衡等挑战仍然存在.
- 现有的方法经常在特征表示和单模输入方面扎.
研究的目的:
- 开发一种使用多输入卷积神经网络 (CNN) 与挤压刺激 (SE) 注意力机制的新型心律失常分类算法.
- 整合ECG信号的多尺度时间频率表示,以增强特征学习.
- 为了提高自动心律失常检测的准确性和稳定性.
主要方法:
- 一个双分支CNN架构处理ECG信号分成两个时间分辨率.
- 使用短时间里埃转换 (STFT) 集成多尺度时间频率表示.
- 挤压激发 (SE) 块来增强通道间的依赖性和特征优先级.
- 一个融合策略,通过双立方互波和元素智能总和结合特征地图.
主要成果:
- 拟议的模型实现了高分类准确性:在MIT-BIH数据库中达到99.13%,在SPH数据库中达到95.84%.
- 获得了优秀的宏观F1分数:94.46% (MIT-BIH) 和95.91% (SPH).
- 该模型的性能优于现有的几种最先进的心律失常分类方法.
结论:
- 新型多输入CNN与SE注意力在心律失常分类中表现出卓越的表现.
- 多尺度特征和注意力机制的整合提高了稳定性和准确性.
- 拟议的算法显示出在心律失常诊断中临床应用的巨大潜力.
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