Dokur Z, Olmez T, Yazgan E, Ersoy O K
Istanbul Technical University, Electrical & Electronics Engineering Department, Turkey.
Med Eng Phys. 1997 Dec;19(8):738-41. doi: 10.1016/s1350-4533(97)00029-5.
In this study, ECG waveform detection was performed by using artificial neural networks (ANNs). Initially, the R peak of the QRS complex is detected, and then feature vectors are formed by using the amplitudes of the significant frequency components of the DFT spectrum. Grow and Learn (GAL) and Kohonen networks are comparatively investigated to detect four different ECG waveforms. The comparative performance results of GAL and Kohonen networks are reported.
在本研究中，通过使用人工神经网络（ANN）进行心电图波形检测。首先，检测QRS复合波的R波峰值，然后利用离散傅里叶变换（DFT）频谱的显著频率分量的幅度形成特征向量。对生长与学习（GAL）网络和Kohonen网络进行了比较研究，以检测四种不同的心电图波形。报告了GAL网络和Kohonen网络的比较性能结果。
