Abstract
The automated diagnostic systems employing diverse and composite features for electrocardiogram (ECG) signals were analyzed and their accuracies were determined. Because of the importance of making the right decision, classification procedures classifying the ECG signals with high accuracy were investigated. The classification accuracies of multilayer perceptron neural network (MLPNN), recurrent neural network (RNN), and mixture of experts (ME) trained on composite features and modified mixture of experts (MME) trained on diverse features were compared. The inputs of these automated diagnostic systems were composed of diverse or composite features (wavelet coefficients and power levels of the power spectral density estimates obtained by the eigenvector methods) and were chosen according to the network structures. The conclusions of this study demonstrated that the MME trained on diverse features achieved accuracy rates which were higher than that of the other automated diagnostic systems trained on composite features.