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

Authors

1 PhD Student, Department of Bioelectrics and Biomedical Engineering, Student Research Committee, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

2 Professor, Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

3 MSc Graduate, Department of Bioelectrics and Biomedical Engineering, Student Research Committee, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

4 MSc Graduate, Student Research Committee, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

5 5- PhD Student, Department of Bioelectrics and Biomedical Engineering, Student Research Committee, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

6 Professor of Cardiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

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.
 

Keywords

References

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Journal of Isfahan Medical School
Volume 40, Issue 661
1st Week, May
May and June 2022
Pages 109-114

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History

  • Receive Date: 02 May 2022
  • Accept Date: 02 May 2022

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