Detection of asphyxiated infant cry using support vector machine integrated with principal component analysis

Asphyxia refers to respiratory failure in infants, a condition caused by inadequate intake of oxygen. It is important to diagnose asphyxia in infants as early as possible, as it could lead to infant morbidity. PCA has the capability to reduce the dimension of input feature vector to SVM. Previous attempts with PCA and SVM to detect asphyxia from baby cries found their principal components in a random manner, which consumes tremendous computation effort and time. Our work here investigates the improvement in performance to detect asphyxia from infant cries by integrating PCA and SVM with a polynomial kernel, with principal components being ranked by EOC, CPV and SCREE methods. Extracted features from the analysis of MFC coefficients are first ranked with the three feature selection algorithms of PCA, before being submitted to SVM for classification. Classification accuracy and support vector are employed to gauge the performance. It is found that the highest classification accuracy and support vector number from classification with support vector machine alone are 93.836% and 335.1, with a second order polynomial kernel and a regularization parameter of 1E-04, while those from CPV and SVM outperformed with CA of 94.172%, a low SV of 254.3, a third order polynomial and regularization parameter of 1E-05.