A Narrative Review on Different Novel Machine Learning Techniques for Detecting Pathologies in Infants From Born Baby Cries

Abstract

This paper reviews the research work on the analysis and classification of pathological infant cries in last 50 years. The literature review mainly covers the need and role of early clinical diagnosis, pathologies detected from cry samples, challenges in pathological cry signal data acquisition, signal processing techniques, and signal classifiers. The signal processing techniques include pre-processing, feature extraction from domains like time, spectral, time-frequency, prosodic, wavelet etc. and feature selection for selecting dominant features. Literature covers traditional machine learning classifiers like bayesian networks, decision trees, K-nearest neighbour, support vector machine, gaussian mixture model etc., and recently added neural network models like convolutional neural networks, regression neural networks, probabilistic neural networks, graph neural networks etc. Significant experimental results of pathological cry identification and classification are listed for comparison. Finally, it suggests future research in direction of database preparation, feature analysis & extraction, neural network classifiers to provide a non-invasive and robust automatic infant cry analysis model.