Classification of infant cries with hypothyroidism using Multilayer Perceptron neural network

Abstract

Hypothyroidism occurs in infants with insufficient production of hormones by the thyroid gland. The cry signals of babies with hypothyroidism have distinct patterns which can be recognized with pattern classifiers such as Multilayer Perceptron (MLP) artificial neural network. This study investigates the performance of the MLP in discriminating between healthy infants and infants suffering from hypothyroidism based on their cries. The infant cries were first divided into one second segments, and important features were extracted using Mel Frequency Cepstrum Coefficient (MFCC) analysis. Two methods were then used to select which MFCC coefficients to be used as features for the MLP: direct selection or Fisher's Ratio analysis (F-ratio analysis). Their performances were compared with experimental results showing that MLP was able to accurately distinguish between the two cases. The classification performance of MLP trained with F-Ratio analysis is found to be better compared to direct selection method.