ANALYZING INFANT CRIES USING A COMMITTEE OF NEURAL NETWORKS IN ORDER TO DETECT HYPOXIA RELATED DISORDER

Abstract

Based on the hypothesis that the sound of the infant cry contains information on the infant's health status, research has been done on how to improve classification of neonate crying sounds into categories called 'normal' and 'abnormal' - the latter referring to some hypoxia-related disorder. Research in this field is hindered by lack of test cases and limited understanding of feature relevance. The research described here combines various ways of dealing with the small data set problem. First, feature pre-selection is done using sequential backwards elimination of possible combinations where the performance of the set of features is tested by a Probabilistic Neural Network which has the advantage of fast learning. Using these features a neural network committee, consisting of Radial Basis Function Neural Networks, was trained on the data, using bootstrapping. This construction yields a multi-classifier system with an overall classification performance of 85% on the so-called "All Cry Units" (ACU) data set, an increase of 34% with respect to the a priori probability of 51%. Several leave-1-out experiments for Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) and Neural Networks (NN) have been conducted in order to compare the performance of the multi-classifier system.