Artificial intelligence framework for heart disease classification from audio signals
Abstract As cardiovascular disorders are prevalent, there is a growing demand for reliable and precise diagnostic methods within this domain. Audio signal-based heart disease detection is a promising area of research that leverages sound signals generated by the heart to identify and diagnose cardio...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-53778-7 |
| _version_ | 1797275003345960960 |
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| author | Sidra Abbas Stephen Ojo Abdullah Al Hejaili Gabriel Avelino Sampedro Ahmad Almadhor Monji Mohamed Zaidi Natalia Kryvinska |
| author_facet | Sidra Abbas Stephen Ojo Abdullah Al Hejaili Gabriel Avelino Sampedro Ahmad Almadhor Monji Mohamed Zaidi Natalia Kryvinska |
| author_sort | Sidra Abbas |
| collection | DOAJ |
| description | Abstract As cardiovascular disorders are prevalent, there is a growing demand for reliable and precise diagnostic methods within this domain. Audio signal-based heart disease detection is a promising area of research that leverages sound signals generated by the heart to identify and diagnose cardiovascular disorders. Machine learning (ML) and deep learning (DL) techniques are pivotal in classifying and identifying heart disease from audio signals. This study investigates ML and DL techniques to detect heart disease by analyzing noisy sound signals. This study employed two subsets of datasets from the PASCAL CHALLENGE having real heart audios. The research process and visually depict signals using spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs). We employ data augmentation to improve the model’s performance by introducing synthetic noise to the heart sound signals. In addition, a feature ensembler is developed to integrate various audio feature extraction techniques. Several machine learning and deep learning classifiers are utilized for heart disease detection. Among the numerous models studied and previous study findings, the multilayer perceptron model performed best, with an accuracy rate of 95.65%. This study demonstrates the potential of this methodology in accurately detecting heart disease from sound signals. These findings present promising opportunities for enhancing medical diagnosis and patient care. |
| first_indexed | 2024-03-07T15:08:06Z |
| format | Article |
| id | doaj.art-517ba2e3aa8942f6b93a0b848a249b3d |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:08:06Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-517ba2e3aa8942f6b93a0b848a249b3d2024-03-05T18:49:22ZengNature PortfolioScientific Reports2045-23222024-02-0114112110.1038/s41598-024-53778-7Artificial intelligence framework for heart disease classification from audio signalsSidra Abbas0Stephen Ojo1Abdullah Al Hejaili2Gabriel Avelino Sampedro3Ahmad Almadhor4Monji Mohamed Zaidi5Natalia Kryvinska6Department of Computer Science, COMSATS University IslamabadDepartment of Electrical and Computer Engineering, College of Engineering AndersonComputer Science Department, Faculty of Computers and Information Technology, University of TabukFaculty of Information and Communication Studies, University of the Philippines Open UniversityDepartment of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf UniversityDepartment of Electrical Engineering, College of Engineering, King Khalid UniversityInformation Systems Department, Faculty of Management, Comenius University in BratislavaAbstract As cardiovascular disorders are prevalent, there is a growing demand for reliable and precise diagnostic methods within this domain. Audio signal-based heart disease detection is a promising area of research that leverages sound signals generated by the heart to identify and diagnose cardiovascular disorders. Machine learning (ML) and deep learning (DL) techniques are pivotal in classifying and identifying heart disease from audio signals. This study investigates ML and DL techniques to detect heart disease by analyzing noisy sound signals. This study employed two subsets of datasets from the PASCAL CHALLENGE having real heart audios. The research process and visually depict signals using spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs). We employ data augmentation to improve the model’s performance by introducing synthetic noise to the heart sound signals. In addition, a feature ensembler is developed to integrate various audio feature extraction techniques. Several machine learning and deep learning classifiers are utilized for heart disease detection. Among the numerous models studied and previous study findings, the multilayer perceptron model performed best, with an accuracy rate of 95.65%. This study demonstrates the potential of this methodology in accurately detecting heart disease from sound signals. These findings present promising opportunities for enhancing medical diagnosis and patient care.https://doi.org/10.1038/s41598-024-53778-7 |
| spellingShingle | Sidra Abbas Stephen Ojo Abdullah Al Hejaili Gabriel Avelino Sampedro Ahmad Almadhor Monji Mohamed Zaidi Natalia Kryvinska Artificial intelligence framework for heart disease classification from audio signals Scientific Reports |
| title | Artificial intelligence framework for heart disease classification from audio signals |
| title_full | Artificial intelligence framework for heart disease classification from audio signals |
| title_fullStr | Artificial intelligence framework for heart disease classification from audio signals |
| title_full_unstemmed | Artificial intelligence framework for heart disease classification from audio signals |
| title_short | Artificial intelligence framework for heart disease classification from audio signals |
| title_sort | artificial intelligence framework for heart disease classification from audio signals |
| url | https://doi.org/10.1038/s41598-024-53778-7 |
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