Ubeyli Elif Derya
Department of Electrical and Electronics Engineering, Faculty of Engineering, TOBB Ekonomi ve Teknoloji Universitesi, 06530 Söğütözü, Ankara, Turkey.
Comput Methods Programs Biomed. 2009 Mar;93(3):313-21. doi: 10.1016/j.cmpb.2008.10.012. Epub 2008 Dec 11.
This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, and atrial fibrillation beat) obtained from the PhysioBank database were classified by four ANFIS classifiers. To improve diagnostic accuracy, the fifth ANFIS classifier (combining ANFIS) was trained using the outputs of the four ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the ECG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals.
本文描述了自适应神经模糊推理系统（ANFIS）模型在心电图（ECG）信号分类中的应用。决策分两个阶段进行：通过计算李雅普诺夫指数进行特征提取，以及使用结合了最小二乘法的反向传播梯度下降法训练的ANFIS进行分类。从生理信号数据库获得的四种类型的心电图搏动（正常搏动、充血性心力衰竭搏动、室性快速心律失常搏动和心房颤动搏动）由四个ANFIS分类器进行分类。为提高诊断准确性，使用四个ANFIS分类器的输出作为输入数据训练第五个ANFIS分类器（组合ANFIS）。所提出的ANFIS模型结合了神经网络的自适应能力和模糊逻辑的定性方法。通过对ANFIS的分析得出了一些关于特征对ECG信号分类显著性的结论。从训练性能和分类准确率方面评估了ANFIS模型的性能，结果证实所提出的ANFIS模型在ECG信号分类方面具有潜力。
