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%.
Infant asphyxia is a condition due to insufficient oxygen intake suffered by newborn babies. A 4 to 9 million occurrences of infant asphyxia are reported each year by WHO. Early diagnosis of asphyxia is important to avoid complications such as damage to the brain, organ and tissue that could lead to fatality. This is possible with the automation of screening of infant asphyxia. Here, a non-invasive Asphyxia Screening Kit is developed. It is a Graphical User Interface that automatically detects asphyxia in infants from early birth to 6 months from their cries and displays the outcome of analysis. It is built with Matlab GUI underlied with signal processing algorithms, capable of achieving a classification accuracy of 96.03%. Successful implementation of ASK will assist to screen infant asphyxia for reference to clinicians for early diagnosis. In addition, ASK also provides an interface to enter patient information and images to be integrated with existing Hospital Information Management System.
The early identification of asphyxiated infants at high risk of adverse outcomes and the early selection of those who might benefit from neuroprotective therapies are required. A prospective observational study was conducted to determine if there were any early clinical, neuroimaging or neurophysiological parameters that might predict the outcome in term newborns with asphyxia.