Our challenge in the current study is to extend research on the cries of newborns for the early diagnosis of different pathologies. This paper proposes a recognition system for healthy and pathological cries using a probabilistic neural network classifier. Two different kinds of features have been used to characterize newborn cry signals: 1) acoustic features such as fundamental frequency glide (F 0glide) and resonance frequencies dysregulation (RFs dys); 2) conventional features such as mel-frequency cestrum coefficients. This paper describes the automatic estimation of the proposed characteristics and the performance evaluation of these features in identifying pathological cries. The adopted methods for F 0glides and RFs dys estimation are based on the derived function of the F 0 contour and the jump "J" of the RFs between two subsequent tunings, respectively. The database used contains 3250 cry samples of full-term and preterm newborns, and includes healthy and pathologic cries. The obtained results indicate the important association between the quantified features and some studied pathologies, and also an improvement in the identification of pathologic cries. The best result obtained is 88.71% for the correct identification of health status of preterm newborns, and 82% for the correct identification of full-term infants with a specific disease. We conclude that using the proposed characteristics improves the diagnosis of pathologies in newborns. Moreover, the method applied in the estimation of these characteristics allows us to extend this study to other uninvestigated pathologies.

Our deduction is that quantification of the variability of these parameters is useful for differentiating the cries of a healthy newborn from those of a newborn with a pathology, and that these data can be used for the early diagnosis of newborn diseases.

The acoustic characteristics of cries are an exhibition of an infant’s health condition and these characteristics have been acknowledged as indicators for various pathologies. This study focused on the detection of infants suffering from sepsis by developing a simplified design using acoustic features and conventional classifiers. The features for the proposed framework were Mel-frequency Cepstral Coefficients (MFCC), Spectral Entropy Cepstral Coefficients (SENCC) and Spectral Centroid Cepstral Coefficients (SCCC), which were classified through K-nearest Neighborhood (KNN) and Support Vector Machine (SVM) classification methods. The performance of the different combinations of the feature sets was also evaluated based on several measures such as accuracy, F1-score and Matthews Correlation Coefficient (MCC). Bayesian Hyperparameter Optimization (BHPO) was employed to tailor the classifiers uniquely to fit each experiment. The proposed methodology was tested on two datasets of expiratory cries (EXP) and voiced inspiratory cries (INSV). The highest accuracy and F-score were 89.99% and 89.70%, respectively. This framework also implemented a novel feature selection method based on Fuzzy Entropy (FE) as a final experiment. By employing FE, the number of features was reduced by more than 40%, whereas the evaluation measures were not hindered for the EXP dataset and were even enhanced for the INSV dataset. Therefore, it was deduced through these experiments that an entropy-based framework is successful for identifying sepsis in neonates and has the advantage of achieving high performance with conventional machine learning (ML) approaches, which makes it a reliable means for the early diagnosis of sepsis in deprived areas of the world.

Several hypotheses have been formulated as a result of observing spectrograms of the audio signals of the newborn infant cry in numerous studies. Our study is based on a few of these hypotheses. The purpose of this article is to differentiate pathological crying from healthy crying through acoustic cry analysis based on neurophysiological parameters of newborns. The automatic estimation of the characteristics of relevant cry signals, such as phonation, hyperphonation, and dysphonation, expressed as percentages, as well as unvoiced sound and mode change percentages, have enabled us to distinguish among the pathologies selected for this study. The results obtained have helped us to make quantitative associations between cry characteristics and pathological conditions affecting newborns.