Wavelet bispectrum-based nonlinear features for cardiac murmur identification
Cardiac or heart sound ca.rries important diagnosis information for several cardiovascular diseases, such as natural or prosthetic valve dysfunction and heart failure. Hence, algorithms are required for the analysis of cardiac sound for computer-based automatic diagnosis. In the cardiac sound-based...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2018-01-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/23311916.2018.1502906 |
| _version_ | 1797705054660067328 |
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| author | Dinesh Kumar Rajendrasinh Jadeja Sarang Pande |
| author_facet | Dinesh Kumar Rajendrasinh Jadeja Sarang Pande |
| author_sort | Dinesh Kumar |
| collection | DOAJ |
| description | Cardiac or heart sound ca.rries important diagnosis information for several cardiovascular diseases, such as natural or prosthetic valve dysfunction and heart failure. Hence, algorithms are required for the analysis of cardiac sound for computer-based automatic diagnosis. In the cardiac sound-based analysis, one of the tasks is to classify abnormal cardiac sounds, i.e. murmurs, caused by various cardiac anomalies. We introduce a new feature named system response which is chosen to be estimated using wavelet bispectrum over Fourier bispectrum because of non-stationary and non-Gaussian nature of cardiac sounds. System response essentially characterizes the cardiac structure responsible for the production of cardiac sounds which is later employed to automatically classify different types of cardiac murmurs. In cases of various types of cardiac murmurs, such as aortic regurgitation and mitral stenosis, system response of the cardiac structures is studied in this paper. Later, an artificial neural network-based classifier is constructed using the system response as a set of features to automatically classify murmur types. Performance of the classifier is obtained with the sensitivity of 93.57% and specificity of 94.24% which is comparable with the sate of the art. Furthermore, system response computed from wavelet bispectrum shows 4% higher accurate classification than Fourier bispectrum. |
| first_indexed | 2024-03-12T05:30:49Z |
| format | Article |
| id | doaj.art-697094015f554d4facb831a15b763e68 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T05:30:49Z |
| publishDate | 2018-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-697094015f554d4facb831a15b763e682023-09-03T06:54:25ZengTaylor & Francis GroupCogent Engineering2331-19162018-01-015110.1080/23311916.2018.15029061502906Wavelet bispectrum-based nonlinear features for cardiac murmur identificationDinesh Kumar0Rajendrasinh Jadeja1Sarang Pande2Marwadi Education Foundation’s Group of InstitutionsMarwadi Education Foundation’s Group of InstitutionsMarwadi Education Foundation’s Group of InstitutionsCardiac or heart sound ca.rries important diagnosis information for several cardiovascular diseases, such as natural or prosthetic valve dysfunction and heart failure. Hence, algorithms are required for the analysis of cardiac sound for computer-based automatic diagnosis. In the cardiac sound-based analysis, one of the tasks is to classify abnormal cardiac sounds, i.e. murmurs, caused by various cardiac anomalies. We introduce a new feature named system response which is chosen to be estimated using wavelet bispectrum over Fourier bispectrum because of non-stationary and non-Gaussian nature of cardiac sounds. System response essentially characterizes the cardiac structure responsible for the production of cardiac sounds which is later employed to automatically classify different types of cardiac murmurs. In cases of various types of cardiac murmurs, such as aortic regurgitation and mitral stenosis, system response of the cardiac structures is studied in this paper. Later, an artificial neural network-based classifier is constructed using the system response as a set of features to automatically classify murmur types. Performance of the classifier is obtained with the sensitivity of 93.57% and specificity of 94.24% which is comparable with the sate of the art. Furthermore, system response computed from wavelet bispectrum shows 4% higher accurate classification than Fourier bispectrum.http://dx.doi.org/10.1080/23311916.2018.1502906cardiac soundbispectrumcepstrumsystem responsehigh order comulants |
| spellingShingle | Dinesh Kumar Rajendrasinh Jadeja Sarang Pande Wavelet bispectrum-based nonlinear features for cardiac murmur identification Cogent Engineering cardiac sound bispectrum cepstrum system response high order comulants |
| title | Wavelet bispectrum-based nonlinear features for cardiac murmur identification |
| title_full | Wavelet bispectrum-based nonlinear features for cardiac murmur identification |
| title_fullStr | Wavelet bispectrum-based nonlinear features for cardiac murmur identification |
| title_full_unstemmed | Wavelet bispectrum-based nonlinear features for cardiac murmur identification |
| title_short | Wavelet bispectrum-based nonlinear features for cardiac murmur identification |
| title_sort | wavelet bispectrum based nonlinear features for cardiac murmur identification |
| topic | cardiac sound bispectrum cepstrum system response high order comulants |
| url | http://dx.doi.org/10.1080/23311916.2018.1502906 |
| work_keys_str_mv | AT dineshkumar waveletbispectrumbasednonlinearfeaturesforcardiacmurmuridentification AT rajendrasinhjadeja waveletbispectrumbasednonlinearfeaturesforcardiacmurmuridentification AT sarangpande waveletbispectrumbasednonlinearfeaturesforcardiacmurmuridentification |