Extraction of Premature Newborns' Spontaneous Cries in the Real Context of Neonatal Intensive Care Units

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

This paper reviews the research work on the analysis and classification of pathological infant cries in the 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 preprocessing, feature extraction from domains, such as time, spectral, time-frequency, prosodic, wavelet, etc, and feature selection for selecting dominant features. Literature covers traditional machine learning classifiers, such as Bayesian networks, decision trees, K-nearest neighbor, support vector machine, Gaussian mixture model, etc, and recently added neural network models, such as 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 the direction of database preparation, feature analysis and extraction, neural network classifiers to provide a non-invasive and robust automatic infant cry analysis model.

A Scene Adaption Framework for Infant Cry Detection in Obstetrics

Infant cry provides useful clinical insights for caregivers to make appropriate medical decisions, such as in obstetrics. However, robust infant cry detection in real clinical settings (e.g. obstetrics) is still challenging due to the limited training data in this scenario. In this paper, we propose a scene adaption framework (SAF) including two different learning stages that can quickly adapt the cry detection model to a new environment. The first stage uses the acoustic principle that mixture sources in audio signals are approximately additive to imitate the sounds in clinical settings using public datasets. The second stage utilizes mutual learning to mine the shared characteristics of infant cry between the clinical setting and public dataset to adapt the scene in an unsupervised manner. The clinical trial was conducted in Obstetrics, where the crying audios from 200 infants were collected. The experimented four classifiers used for infant cry detection have nearly 30% improvement on the F1-score by using SAF, which achieves similar performance as the supervised learning based on the target setting. SAF is demonstrated to be an effective plug- and-play tool for improving infant cry detection in new clinical settings. Our code is available at https://github.com/contactless-healthcare/Scene-Adaption-for-Infant-Cry-Detection.

Can you Understand why I am Crying? A Decision-making System for Classifying Infants' Cry Languages Based on deepSVM Model

There scientific and therapeutic advances in perinatology and neonatology have improved the survival prospects of preterm and extremely low birth weight infants. Infants' cries are a valuable noninvasive tool for monitoring their neurologic health, especially if they are premature. An automatic acoustic analysis and data mining are employed in this study to determine the discriminative features of preterm and full-term infant cries. The use of machine learning for recognizing sounds in a newborn's cry language has received less attention than previous methods for analyzing the sounds. Moreover, to extract appropriate features from infant cries, adequate knowledge and appropriate signal descriptors are required. Accordingly, to analyze infant cry language, we propose an approach that uses fractal descriptor to extract discriminant features from spectrograms of windowed signals, followed by iterative neighborhood component analysis (iNCA) to select appropriate features. Additionally, the improved deep support vector machine (deepSVM) is used to classify the infants' crying types and their meanings. The proposed method is verified using a newborn sound dataset. According to the classification of five types of crying perception based on various characteristics, 98.34% of all crying perceptions have been recognized. Although there are many classes examined, the feature extraction method based on the fractal method and our optimal classification have a much higher diagnostic accuracy compared to similar methods for analyzing baby crying language. The proposed method can overcome many problems associated with analyzing babies' crying sounds and understanding their language, like uncertainty and unusual errors in classification.