This paper presents a new method for the analysis of newborn infant cry to detect hypothyroidism. Hypothyroidism is a condition caused by insufficient production of thyroid hormone by the thyroid gland. The proposed technique is robust as it automatically extracts and analyzes features of infant cry signal. In preprocessing of the signals, separation of voice or unvoiced sections is implemented with automatic segmentation that integrates zero rate crossing and short time energy methods. For feature extraction, the Mel frequency cepstrum coefficient analysis is used to extract main features from the infant cry signal. Then, similarities between the two types of signals and significant information are studied using the F-Ratio analysis. Results show that the F-Ratio analysis was able to discriminate between essential and non-essential features for classification.
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%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.