title: Performance of Combined Support Vector Machine and Principal Component Analysis in recognizing infant cry with asphyxia.
abstract: 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 SVM-PCA is 95.86%.
authors.affiliation: Faculty of Electrical Engineering, University Teknologi Mara, 40450 Shah Alam, Selangor, Malaysia.
authors.forename: R
authors.initials: R
authors.lastname: Sahak
authors.forename: W
authors.initials: W
authors.lastname: Mansor
authors.forename: Y K
authors.initials: YK
authors.lastname: Lee
authors.forename: A M
authors.initials: AM
authors.lastname: Yassin
authors.forename: A
authors.initials: A
authors.lastname: Zabidi
mesh_terms: D000465:Algorithms; D001238:Asphyxia Neonatorum / Q000175:diagnosis*; D003448:Crying; D006801:Humans; D007231:Infant, Newborn; D025341:Principal Component Analysis / Q000379:methods*; D012984:Software