Neagoe Victor -Emil, Iatan Iuliana -Florentina, Grunwald Sorin
Dept. of Applied Electronics and Information Engineering, Polytechnic University, Bucharest, Romania.
AMIA Annu Symp Proc. 2003;2003:494-8.
The paper focuses on the neuro-fuzzy classifier called Fuzzy-Gaussian Neural Network (FGNN) to recognize the ECG signals for Ischemic Heart Disease (IHD) diagnosis. The proposed ECG processing cascade has two main stages: (a) Feature extraction from the QRST zone of ECG signals using either the Principal Component Analysis (PCA) or the Discrete Cosine Transform (DCT); (b) Pattern classification for IHD diagnosis using the FGNN. We have performed the software implementation and have experimented the proposed neuro-fuzzy model for IHD diagnosis. We have used an ECG database of 40 subjects, where 20 subjects are IHD patients and the other 20 are normal ones. The best performance has been of 100% IHD recognition score. The result is exciting as much as we have used only one lead (V5) of ECG records as input data, while the current diagnosis approaches require the set of 12 lead ECG signals!
本文重点研究了一种名为模糊高斯神经网络（FGNN）的神经模糊分类器，用于识别心电图信号以诊断缺血性心脏病（IHD）。所提出的心电图处理级联有两个主要阶段：（a）使用主成分分析（PCA）或离散余弦变换（DCT）从心电图信号的QRST波段进行特征提取；（b）使用FGNN进行IHD诊断的模式分类。我们进行了软件实现，并对所提出的用于IHD诊断的神经模糊模型进行了实验。我们使用了一个包含40名受试者的心电图数据库，其中20名受试者是IHD患者，另外20名是正常人。最佳性能是IHD识别分数达到100%。这个结果令人兴奋，因为我们仅使用了心电图记录的一个导联（V5）作为输入数据，而当前的诊断方法需要12导联的心电图信号集！
