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A New Approach of Phonocardiogram Analysis for Screening Some of Cardio-Vascular Diseases Based on Deep Learning Publisher



Mohammadi E1 ; Kermani S2 ; Nourianzavareh M1 ; Zare A3 ; Aghapanahroudsari H1 ; Samieinasab M1 ; Sanei H4
Authors
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Authors Affiliations
  1. 1. Department of Bioelectrics and Biomedical Engineering, Student Research Committee, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  3. 3. Student Research Committee, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  4. 4. Cardiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Journal of Isfahan Medical School Published:2022


Abstract

Background: Cardiovascular diseases are one of the leading causes of death worldwide. Therefore, early diagnosis of heart disease, evaluation of cardiovascular system using cardiac hearing and Phonocardiogram (PCG) analysis which is a low cost, non-invasive, rapid method, and automatic screening of cardiovascular patients in remote areas is crucial. The aim of this study is to present a new method for screening heart patients based on signal processing (PCG) that is cheap and fast and has sufficient accuracy. Methods: In this study, for screening 2062 labeled PCG signals, by extracting new features and applying them in 1- Random forest network 2- K-nearest neighbors 3- Decision tree 4- Linear discriminant analysis 5- Logistic regression and 6- Deep neural network, six different models were constructed and each of them was evaluated by k fold cross-validation method (K = 10). The test data were applied to the mentioned models and based on the outputs of these models, three indicators of accuracy, sensitivity and specificity were calculated. We showed and developed a new solution in differentiating and screening some heart patients from healthy individuals using PCG analysis. Findings: Evaluation on the mentioned models was calculated by the three indicators, repeated 5 times and their mean and variance values were calculated. The highest sensitivity value is related to deep neural network (DNN) with sensitivity of 96.4 ± 0.14 and accuracy of 93.4 ± 0.11. Conclusion: The new differential features along with the success of the proposed deep neural network in differentiating and screening between PCGs of healthy individuals and heart patients, shows the efficiency of the proposed algorithm. This method can be further improved with simultaneous multimodal classifier and the application of the voting rule. © 2022 Isfahan University of Medical Sciences(IUMS). All rights reserved.
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