Automatic heart diseases detection techniques using musical approaches
In this study, a musical approach to provide an automatic heart disease detection system is proposed. Heart sounds are recorded with audio format. Audio files are converted to semi-structured music files that can be represented textually. Samples were captured from different heart diseases and were...
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| Format: | Article |
| Language: | English |
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Asian Network for Scientific Information
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/22459/ http://psasir.upm.edu.my/id/eprint/22459/1/Automatic%20heart%20diseases%20detection%20techniques%20using%20musical%20approaches.pdf |
| _version_ | 1848844490333224960 |
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| author | Khorasani, Ehsan Safar C. Doraisamy, Shyamala Azman, Azreen |
| author_facet | Khorasani, Ehsan Safar C. Doraisamy, Shyamala Azman, Azreen |
| author_sort | Khorasani, Ehsan Safar |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In this study, a musical approach to provide an automatic heart disease detection system is proposed. Heart sounds are recorded with audio format. Audio files are converted to semi-structured music files that can be represented textually. Samples were captured from different heart diseases and were stored in a database. Two different approaches which are information retrieval based on n-gram and longest common subsequence are used to retrieve the similarity of a given sample with existing heart diseases in the database. Since the frequency of heart sound is relative to age and physical characteristics of a patient, an important feature of using n-gram in this study is to retrieve diseases without respect to the different heart sounds frequencies. The effects of window sizes for n-gram approach on the accuracy of the information retrieval were tested and a proper window size was extracted. The results of the performed experiments showed that window size of 5 notes revealed a high performance in comparison with other window sizes. Hence, the proposed technique can detect and recognize a heart disease with a reliable accuracy. Average of precision values for around 85% in information retrieval and 55% in longest common subsequence technique were obtained for the retrieval of heart sound categories. Moreover, the results of string matching technique demonstrated that threshold level of 65% could appropriately detect heart disease. |
| first_indexed | 2025-11-15T08:31:45Z |
| format | Article |
| id | upm-22459 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T08:31:45Z |
| publishDate | 2011 |
| publisher | Asian Network for Scientific Information |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-224592016-06-08T08:57:03Z http://psasir.upm.edu.my/id/eprint/22459/ Automatic heart diseases detection techniques using musical approaches Khorasani, Ehsan Safar C. Doraisamy, Shyamala Azman, Azreen In this study, a musical approach to provide an automatic heart disease detection system is proposed. Heart sounds are recorded with audio format. Audio files are converted to semi-structured music files that can be represented textually. Samples were captured from different heart diseases and were stored in a database. Two different approaches which are information retrieval based on n-gram and longest common subsequence are used to retrieve the similarity of a given sample with existing heart diseases in the database. Since the frequency of heart sound is relative to age and physical characteristics of a patient, an important feature of using n-gram in this study is to retrieve diseases without respect to the different heart sounds frequencies. The effects of window sizes for n-gram approach on the accuracy of the information retrieval were tested and a proper window size was extracted. The results of the performed experiments showed that window size of 5 notes revealed a high performance in comparison with other window sizes. Hence, the proposed technique can detect and recognize a heart disease with a reliable accuracy. Average of precision values for around 85% in information retrieval and 55% in longest common subsequence technique were obtained for the retrieval of heart sound categories. Moreover, the results of string matching technique demonstrated that threshold level of 65% could appropriately detect heart disease. Asian Network for Scientific Information 2011 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/22459/1/Automatic%20heart%20diseases%20detection%20techniques%20using%20musical%20approaches.pdf Khorasani, Ehsan Safar and C. Doraisamy, Shyamala and Azman, Azreen (2011) Automatic heart diseases detection techniques using musical approaches. Journal of Applied Sciences, 11 (17). pp. 3161-3168. ISSN 1812-5654; ESSN: 1812-5662 http://scialert.net/abstract/?doi=jas.2011.3161.3168 10.3923/jas.2011.3161.3168 |
| spellingShingle | Khorasani, Ehsan Safar C. Doraisamy, Shyamala Azman, Azreen Automatic heart diseases detection techniques using musical approaches |
| title | Automatic heart diseases detection techniques using musical approaches |
| title_full | Automatic heart diseases detection techniques using musical approaches |
| title_fullStr | Automatic heart diseases detection techniques using musical approaches |
| title_full_unstemmed | Automatic heart diseases detection techniques using musical approaches |
| title_short | Automatic heart diseases detection techniques using musical approaches |
| title_sort | automatic heart diseases detection techniques using musical approaches |
| url | http://psasir.upm.edu.my/id/eprint/22459/ http://psasir.upm.edu.my/id/eprint/22459/ http://psasir.upm.edu.my/id/eprint/22459/ http://psasir.upm.edu.my/id/eprint/22459/1/Automatic%20heart%20diseases%20detection%20techniques%20using%20musical%20approaches.pdf |