Volume 17, Issue 3 (11-2020)                   JSDP 2020, 17(3): 157-176 | Back to browse issues page


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Mokhlessi O, Seyed Mahdavi Chabok S, Alirezaee A. Selecting effective features from Phonocardiography by Genetic Algorithm based on Pearson`s Coefficients Correlation. JSDP. 2020; 17 (3) :157-176
URL: http://jsdp.rcisp.ac.ir/article-1-508-en.html
Faculty of Electrical Engineering, Islamic Azad University, Mashhad
Abstract:   (238 Views)

The heart is one of the most important organs in the body, which is responsible for pumping blood into the valvular systems. Beside, heart valve disorders are one of the leading causes of death in the world. These disorders are complications in the heart valves that cause the valves to deform or damage, and as a result, the sounds caused by their opening and closing compared to a healthy heart.
Obviously, due to the complexities of cardiac audio signals and their recording, designing an accurate diagnosis system free of noise and fast enough is difficult to achieve. One of the most important issues in designing an intelligent heart disease diagnosis system is the use of appropriate primary data. This means that these data must not only be recorded according to the patientchr('39')s equipment and clinical condition, but also must be labeled according to the correct diagnosis of the physician.
However, in this study, an attempt has been made to provide an intelligent system for diagnosing valvular heart failure using phonocardiographic sound signals to have maximum diagnostic power. For this purpose, the signals are labeled and used under the supervision of a specialist doctor.
The main goal is to select the effective feature vectors using the genetic optimization method and also based on the evaluation function by Pearson correlation coefficients.
Before extraction feature step, preprocessing from data recording, normalization, segmentation, and filtering were used to increase system performance accuracy. For better result, Signal temporal, wavelet and signal energy components are extracted from the prepared signal as feature extraction step.
Whereas extracted problem space were not correlated enough, in next step principal component analysis, linear separator analysis, and uncorrelated linear separator analysis methods were used to make feature vectors in a final correlated space.
In selecting step, an efficient and simple method is used inorder to estimate the number of optimal features. In general, correlation is a criterion for determining the relationship between variables. The difference between the correlations of all feature subsets is calculated (for both in-class and out-of-class subsets) and then categorized in descending order according to the evaluation function.
As a result, in the feature selection step the evaluation function is based on the Pearson statistical method, which is evaluated by a genetic algorithm with the aim of identifying more effective and correlated features in the final vectors.
Eventually In this paper, two widely used neural networks with dynamic and static structure including perceptron and Elman neural networks have been used to evaluate the accuracy of the proposed vectors. The results of modeling the process of selecting effective features and diagnosing the disease show the efficiency of the proposed method.

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Type of Study: Research | Subject: Paper
Received: 2016/04/25 | Accepted: 2020/08/19 | Published: 2020/12/5 | ePublished: 2020/12/5

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