Adaptive Detection of Defects in the Ventricular Heart Disease: A Model with the Possibility of Automatic Analysis of Audio Signals through the Heart

In this paper, a hybrid algorithm has been developed by analyzing the audio signals of the heart, that consists of extracting features based on chaos technique, reducing dimensions and analyzing the main components and classifying outputs by relying on comparative neuro-fuzzy networks. Uncertainty and high error in the diagnosis of inter-ventricular openings are one of the common problems with the previous methods. Due to the importance of the auto-diagnosis of this heart condition, it is necessary to be well-designed and far from error. Transmission of feature spaces to their mapping by the main component analysis algorithm is made by two steps, selecting the number of 18 to 25 attributes among about 50 extracted attributes that these informations are input of the class. The proposed classification classifies the adaptive fuzzy neural network system with the possibility of predicting the incidence of heart disease, which predicts the number of repetitions at the acceptable level of outputs by entering the data. The data are from the Umich database at the University of Michigan and include samples from the ventricular aperture. The ratio of data split in the learning and testing phase is from 0.9 to 0.1 (cross-check), and the K-fold validation method is used. Calculation of criteria such as accuracy, sensitivity and uncertainty by the concept of entropy in a hybrid algorithm suggests the proper performance of the proposed method.