Neural network recognition algorithm of breath sounds based on SVM
A SVM neural network (support vector machines) for breath sounds recognition algorithm was advanced,breath sounds feature obtained through wavelet analysis were input into neural networks and carried on the training to the training samples as a feature of SVM method input in order to classify the te...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2014-10-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.10.025/ |
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| _version_ | 1841539755749146624 |
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| author | Gou-dong LIU Jing XU |
| author_facet | Gou-dong LIU Jing XU |
| author_sort | Gou-dong LIU |
| collection | DOAJ |
| description | A SVM neural network (support vector machines) for breath sounds recognition algorithm was advanced,breath sounds feature obtained through wavelet analysis were input into neural networks and carried on the training to the training samples as a feature of SVM method input in order to classify the test samples.Three States (normal,mild and severe lesions) of breath sounds were recognized,and K nearest neighbor (KNN) methods are compared .The results show that SVM method has a higher recognition accuracy and can be used to recognize different breath sounds,which settled the local extremum problem that cannot be avoided in the neural network method and provide an effective algorithm for information processing in body area network technology. |
| format | Article |
| id | doaj-art-a60c778c5ec5450d889d12812b089e44 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2014-10-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-a60c778c5ec5450d889d12812b089e442025-01-14T06:44:29ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2014-10-013521822259687124Neural network recognition algorithm of breath sounds based on SVMGou-dong LIUJing XUA SVM neural network (support vector machines) for breath sounds recognition algorithm was advanced,breath sounds feature obtained through wavelet analysis were input into neural networks and carried on the training to the training samples as a feature of SVM method input in order to classify the test samples.Three States (normal,mild and severe lesions) of breath sounds were recognized,and K nearest neighbor (KNN) methods are compared .The results show that SVM method has a higher recognition accuracy and can be used to recognize different breath sounds,which settled the local extremum problem that cannot be avoided in the neural network method and provide an effective algorithm for information processing in body area network technology.http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.10.025/support vector machinebreath soundswavelet analysisneural networkbody area network |
| spellingShingle | Gou-dong LIU Jing XU Neural network recognition algorithm of breath sounds based on SVM Tongxin xuebao support vector machine breath sounds wavelet analysis neural network body area network |
| title | Neural network recognition algorithm of breath sounds based on SVM |
| title_full | Neural network recognition algorithm of breath sounds based on SVM |
| title_fullStr | Neural network recognition algorithm of breath sounds based on SVM |
| title_full_unstemmed | Neural network recognition algorithm of breath sounds based on SVM |
| title_short | Neural network recognition algorithm of breath sounds based on SVM |
| title_sort | neural network recognition algorithm of breath sounds based on svm |
| topic | support vector machine breath sounds wavelet analysis neural network body area network |
| url | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.10.025/ |
| work_keys_str_mv | AT goudongliu neuralnetworkrecognitionalgorithmofbreathsoundsbasedonsvm AT jingxu neuralnetworkrecognitionalgorithmofbreathsoundsbasedonsvm |