Ubeyli Elif Derya
Department of Electrical and Electronics Engineering, Faculty of Engineering, TOBB Ekonomi ve Teknoloji Universitesi, 06530 Söğütözü, Ankara, Turkey.
Comput Biol Med. 2008 Mar;38(3):401-10. doi: 10.1016/j.compbiomed.2008.01.002. Epub 2008 Feb 14.
The aim of this study is to evaluate the diagnostic accuracy of the recurrent neural networks (RNNs) with composite features (wavelet coefficients and Lyapunov exponents) on the electrocardiogram (ECG) signals. Two types of ECG beats (normal and partial epilepsy) were obtained from the MIT-BIH database. The multilayer perceptron neural networks (MLPNNs) were also tested and benchmarked for their performance on the classification of the ECG signals. Decision making was performed in two stages: computing composite features which were then input into the classifiers and classification using the classifiers trained with the Levenberg-Marquardt algorithm. The research demonstrated that the wavelet coefficients and the Lyapunov exponents are the features which well represent the ECG signals and the RNN trained on these features achieved high classification accuracies.
本研究的目的是评估具有复合特征（小波系数和李雅普诺夫指数）的递归神经网络（RNN）对心电图（ECG）信号的诊断准确性。从麻省理工学院 - 贝斯以色列女执事医疗中心（MIT - BIH）数据库中获取了两种类型的心电图搏动（正常和部分癫痫）。还对多层感知器神经网络（MLPNN）在心电图信号分类方面的性能进行了测试和基准评估。决策分两个阶段进行：计算复合特征，然后将其输入到分类器中，并使用通过列文伯格 - 马夸特算法训练的分类器进行分类。研究表明，小波系数和李雅普诺夫指数是很好地表示心电图信号的特征，基于这些特征训练的RNN实现了高分类准确率。
