Phonocardiogram classification using deep neural networks and weighted probability comparisons
| Autor: | Misael Mondragón, Miguel Sotaquira, Demián Alvear |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
0206 medical engineering Biomedical Engineering Sample (statistics) 02 engineering and technology Cardiac auscultation Deep Learning 0202 electrical engineering electronic engineering information engineering Sensitivity (control systems) Probability Phonocardiogram Cardiac cycle business.industry Phonocardiography Signal Processing Computer-Assisted Pattern recognition General Medicine Repeatability 020601 biomedical engineering Heart Sounds Heart sounds Deep neural networks 020201 artificial intelligence & image processing Artificial intelligence business Algorithms |
| Zdroj: | Journal of Medical Engineering & Technology. 42:510-517 |
| ISSN: | 1464-522X 0309-1902 |
| DOI: | 10.1080/03091902.2019.1576789 |
| Popis: | Cardiac auscultation is one of the most conventional approaches for the initial assessment of heart disease, however the technique is highly user-dependent and with low repeatability. Several computational approaches based on the analysis of the phonocardiograms (PCG) have been proposed to classify heart sounds into normal or abnormal, but most often do not achieve acceptable levels of sensitivity (Se) and specificity (Sp) or require the use of special hardware. We propose a novel approach for classification of PCG. First, the system makes use of deep neural networks for computing individual cardiac cycle probabilities, followed by classification using weighted probability comparisons. The system was tested on an extended dataset consisting of a balanced sample of 18179 normal and abnormal cycles, achieving Se and Sp values of 91.3% and 93.8% respectively. In addition, the system overcomes previous limitations since it was trained with a balanced sample; also, the decision factor used during the classification stage allows to control the trade-off between Se and Sp, making the proposed system suitable for clinical applications. |
| Databáze: | OpenAIRE |
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