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首页> 外文会议>Proceedings of the 17th IASTED international conference on robotics applications ; Proceedings of the 11th IASTED international conference on biomedical engineering >CAD DETECTION USING NEURAL NETWORK FUSION OF THE 12 LEAD STRESS ECG SYSTEM
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CAD DETECTION USING NEURAL NETWORK FUSION OF THE 12 LEAD STRESS ECG SYSTEM

机译：利用神经网络融合的12导联心电图进行CAD检测

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摘要

In this paper, we develop and test a system for integrating transformed information of the 12 lead stress ECG signals, at the classifier real-valued output level. A coronary artery disease data set was collected and utilized in this study. Four types of features were extracted using the discrete cosine transform, two levels of the discrete wavelet transform, and dimensionality-reduced data using principle component analysis. For each feature type, 12 neural networks were trained and tested using the backpropagation algorithm. Several experiments have been conducted to test this system. Results have demonstrated superior performance when using a fusion of 12 classifier output values, compared to single lead classifier systems. We observed that a 3-level discrete wavelet transform has computed 95-100% performance success rates, using sensitivity, specificity, or accuracy.
机译：在本文中，我们开发并测试了一种用于在分类器实际值输出水平上集成12个主应力ECG信号的转换信息的系统。收集冠状动脉疾病数据集并用于本研究。使用离散余弦变换提取四种类型的特征，使用离散小波变换提取两个级别的特征，并使用主成分分析提取降维数据。对于每种特征类型，使用反向传播算法训练和测试了12个神经网络。已经进行了一些实验来测试该系统。与单导程分类器系统相比，当使用12个分类器输出值的融合结果显示出了卓越的性能。我们观察到，使用敏感性，特异性或准确性，三级离散小波变换已计算出95-100％的性能成功率。

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