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使用ITD,DWT和基于心电图信号的确定性学习来检测心肌梗塞

Wei Zeng¹, Chengzhi Yuan²

¹School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan, 364012 People's Republic of China.

Cognitive neurodynamics

|July 31, 2023

概括

这项研究引入了一种自动化方法,用于检测心肌梗塞 (MI) 使用合成心电图 (ECG) 和心电图 (VCG) 数据. 这种新的技术实现了高精度,为MI诊断提供了潜在的临床工具.

科学领域:

心脏病学心脏病学
生物医学信号处理
人工智能的人工智能

背景情况:

心血管疾病 (CVD),特别是心肌梗塞 (MI),是全球主要的死亡原因.
目前的MI诊断依赖于对心电图 (ECG) 和心电图 (VCG) 信号的主观视觉检查,这是耗时且容易出现错误的.
需要自动检测方法来克服传统的MI诊断的局限性.

研究的目的:

开发一种用于检测心肌梗塞 (MI) 的新型自动化技术.
为了合成12导电心电图和弗兰克XYZ导电图,创建了一个混合心脏向量,用于增强分析.
为了利用先进的信号处理和人工智能进行准确的MI分类.

主要方法:

从12导心电图和弗兰克XYZ导线合成了一个4维心脏向量.
应用内在时间尺度分解 (ITD) 来提取反映心脏动态的正确旋转元件 (PRC).
利用离散波波变换 (DWT) 和3D相位空间重建,从主要的PRC中提取特征.
使用神经网络对健康和心脏病发作心脏载体信号进行分类.

主要成果:

拟议的方法在PhysioNet PTB数据库中实现了98.20%的平均分类准确度.
实验包括148名心脏病患者和52名健康对照者的数据,使用十倍交叉验证.
该技术有效地捕捉了健康和心脏病发作患者之间心脏系统动态的差异.

结论:

开发的方法证明了自动MI检测的高效性.
合成心脏载体并分析其动态的新方法显示出临床应用的前景.
这种技术在客观和有效诊断心肌梗塞方面提供了潜在的进步.

关键词:

心脏系统动态心脏系统动态确定性学习学习是决定性的.离散波形变换 (DWT) 是指离散波形变换.电心电图 (ECG) 是指心电图.内在时间尺度分解 (ITD)心肌梗塞 (MI) 检测检测神经网络的神经网络的神经网络

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