Performance of Combined Support Vector Machine and Principal Component Analysis in recognizing infant cry with asphyxia
Combined Support Vector Machine (SVM) and Principal Component Analysis (PCA) was used to recognize the infant cries with asphyxia. SVM classifier based on features selected by the PCA was trained to differentiate between pathological and healthy cries. The PCA was applied to reduce dimensionality of...
| Published in: | 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 |
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| Main Author: | |
| Format: | Conference paper |
| Language: | English |
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2010
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650820934&doi=10.1109%2fIEMBS.2010.5628084&partnerID=40&md5=7fe12f5567f7dfe09dd98f1ee1a9a665 |
| Summary: | Combined Support Vector Machine (SVM) and Principal Component Analysis (PCA) was used to recognize the infant cries with asphyxia. SVM classifier based on features selected by the PCA was trained to differentiate between pathological and healthy cries. The PCA was applied to reduce dimensionality of the vectors that serve as inputs to the SVM. The performance of the SVM utilizing linear and RBF kernel was examined. Experimental results showed that SVM with RBF kernel yields good performance. The classification accuracy in classifying infant cry with asphyxia using the SVMPCA is 95.86%. © 2010 IEEE. |
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| DOI: | 10.1109/IEMBS.2010.5628084 |