A Novel Deep Learning CNN for Heart Valve Disease Classification Using Valve Sound Detection
Valve sounds are mostly a result of heart valves opening and closing. Laminar blood flow is interrupted and abruptly transforms into turbulent flow, causing some sounds, and is explained by improper valve operation. It has been feasible to demonstrate that the typical and compulsive instances are di...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/4/846 |
| _version_ | 1797621402806779904 |
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| author | Randa I. Aljohani Hanan A. Hosni Mahmoud Alaaeldin Hafez Magdy Bayoumi |
| author_facet | Randa I. Aljohani Hanan A. Hosni Mahmoud Alaaeldin Hafez Magdy Bayoumi |
| author_sort | Randa I. Aljohani |
| collection | DOAJ |
| description | Valve sounds are mostly a result of heart valves opening and closing. Laminar blood flow is interrupted and abruptly transforms into turbulent flow, causing some sounds, and is explained by improper valve operation. It has been feasible to demonstrate that the typical and compulsive instances are different for both chronological and spatial aspects through the examination of phono-cardiographic signals. The current work presents the development and application of deep convolutional neural networks for the binary and multiclass categorization of multiple prevalent valve diseases and typical valve sounds. Three alternative methods were taken into consideration for feature extraction: mel-frequency cepstral coefficients and discrete wavelet transform. The precision of both models accomplished F1 scores of more than 98.2% and specificities of more than 98.5%, which reflects the instances that can be wrongly classified as regular. These experimental results prove the proposed model as a highly accurate assisted diagnosis model. |
| first_indexed | 2024-03-11T08:55:25Z |
| format | Article |
| id | doaj.art-e37947d435e1477b8eedf55279b0186c |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T08:55:25Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-e37947d435e1477b8eedf55279b0186c2023-11-16T20:10:55ZengMDPI AGElectronics2079-92922023-02-0112484610.3390/electronics12040846A Novel Deep Learning CNN for Heart Valve Disease Classification Using Valve Sound DetectionRanda I. Aljohani0Hanan A. Hosni Mahmoud1Alaaeldin Hafez2Magdy Bayoumi3Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi ArabiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaDepartment of Computer Engineering, College of Computer Science, University of Louisiana at Lafayette, Lafayette, LA 70504, USAValve sounds are mostly a result of heart valves opening and closing. Laminar blood flow is interrupted and abruptly transforms into turbulent flow, causing some sounds, and is explained by improper valve operation. It has been feasible to demonstrate that the typical and compulsive instances are different for both chronological and spatial aspects through the examination of phono-cardiographic signals. The current work presents the development and application of deep convolutional neural networks for the binary and multiclass categorization of multiple prevalent valve diseases and typical valve sounds. Three alternative methods were taken into consideration for feature extraction: mel-frequency cepstral coefficients and discrete wavelet transform. The precision of both models accomplished F1 scores of more than 98.2% and specificities of more than 98.5%, which reflects the instances that can be wrongly classified as regular. These experimental results prove the proposed model as a highly accurate assisted diagnosis model.https://www.mdpi.com/2079-9292/12/4/846deep learningheart diseasesound classification |
| spellingShingle | Randa I. Aljohani Hanan A. Hosni Mahmoud Alaaeldin Hafez Magdy Bayoumi A Novel Deep Learning CNN for Heart Valve Disease Classification Using Valve Sound Detection Electronics deep learning heart disease sound classification |
| title | A Novel Deep Learning CNN for Heart Valve Disease Classification Using Valve Sound Detection |
| title_full | A Novel Deep Learning CNN for Heart Valve Disease Classification Using Valve Sound Detection |
| title_fullStr | A Novel Deep Learning CNN for Heart Valve Disease Classification Using Valve Sound Detection |
| title_full_unstemmed | A Novel Deep Learning CNN for Heart Valve Disease Classification Using Valve Sound Detection |
| title_short | A Novel Deep Learning CNN for Heart Valve Disease Classification Using Valve Sound Detection |
| title_sort | novel deep learning cnn for heart valve disease classification using valve sound detection |
| topic | deep learning heart disease sound classification |
| url | https://www.mdpi.com/2079-9292/12/4/846 |
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