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使用多角度双通道融合网络自动识别心房动和心室动

Weiyi Yang¹, Di Wang², Wei Fan³

¹School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China.

Artificial intelligence in medicine

|November 4, 2023

概括

一个新的多角度双通道融合网络 (MDF-Net) 准确地检测心房动 (AFIB) 和心室动 (VFIB) 从ECG. 这种人工智能工具提供更快的训练和可靠的诊断,即使有噪音或不平衡的数据.

科学领域:

心血管医学和人工智能
生物医学信号处理

背景情况:

心房动 (AFIB) 和心室动 (VFIB) 是全球死亡的主要原因.
电心电图 (ECG) 对于诊断AFIB和VFIB至关重要,但微妙的变化挑战视觉解释.
需要自动诊断工具来提高检测这些心律失常的准确性和效率.

研究的目的:

开发和验证一种新的深度学习模型,用于自动识别AFIB和VFIB心跳.
通过融合从双导电图信号中提取的多角度特征来提高诊断准确性.
创建一个计算高效的诊断工具,用于临床应用.

主要方法:

提出了一个多角度双通道融合网络 (MDF-Net),集成与任务相关的组件分析 (TRCA) - 主要组件分析 (PCA) 和正规相关性分析 (CCA) - PCA网络.
采用TRPC-Net和CPC-Net从双轴ECG中进行深度特征提取,使多角度特征级融合成为可能.
使用线性支向量的机器加权软max与平均值 (LS-WSA) 进行决策级融合和分类.

主要成果:

实现了高诊断准确率99.39%在患者内和97.17%在患者间的实验,以区分AFIB和VFIB.
在杂的心电图数据上表现出强大的性能,尽管有信号干扰,但仍然保持了准确性.
在极度不平衡的数据集上展示了有效性,准确地识别了罕见的异常心跳.

结论:

拟议的MDF-Net提供了一种高度准确和高效的方法,用于自动检测ECG的AFIB和VFIB.
融合策略有效地解决了微妙的ECG变化和噪音数据所带来的挑战.
MDF-Net显示出作为改善心血管疾病管理的临床诊断工具的巨大潜力.

关键词:

在CPC-Net中,使用的是CPC-Net.在ECG分类中使用ECG分类.在MDF-Net的基础上.这是TRPC-Net.权重软最大值与平均平均值.

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