Choice for a support vector machine kernel function for recognizing asphyxia from infant cries

This paper investigates the performance of several kernel functions of support vector machine in detecting asphyxia from infant cries. In this study, Mel frequency cepstrum coefficients derived from the recorded infant cries were used as the input vectors. These input vectors were trained and classified using support vector machine. Four types of kernels - linear, quadratic, polynomial and radial basic function, were experimented and compared. Accuracy, sensitivity and specificity were adopted as criteria to obtain the best kernel. Experimental results showed that radial basic function kernel (σ = 35) is the best kernel with an accuracy of 85.15%, sensitivity of 91% and specificity of 71%.

[1]  L. Huanxiang,et al.  Support vector machines classification for discriminating coronary heart disease patients from non-coronary heart disease. , 2007, The West Indian medical journal.

[2]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[3]  Xiao Sun,et al.  Support vector machine for classification of meiotic recombination hotspots and coldspots in Saccharomyces cerevisiae based on codon composition , 2005, BMC Bioinformatics.

[4]  William Stafford Noble,et al.  Support vector machine , 2013 .

[5]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[6]  G. Várallyay.  The melody of crying. , 2007, International journal of pediatric otorhinolaryngology.

[7]  Orion F. Reyes-Galaviz,et al.  A System for the Processing of Infant Cry to Recognize Pathologies in Recently Born Babies with Neural Networks , 2004 .

[8]  G. Várallyay,et al.  FUTURE PROSPECTS OF THE APPLICATION OF THE INFANT CRY IN THE MEDICINE , 2006 .

[9]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[10]  Jiangming Kan,et al.  Use of Support Vector Machines in recognition of fork branches of the standing trees , 2007, 2007 2nd IEEE Conference on Industrial Electronics and Applications.

[11]  Joseph Soltis,et al.  The signal functions of early infant crying , 2004, Behavioral and Brain Sciences.

[12]  Alfred S. Malowany,et al.  Classification of infant cry vocalizations using artificial neural networks (ANNs) , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[13]  Nello Cristianini,et al.  Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..