Use of psychoacoustic spectrum warping, decision template fusion, and neighborhood component analysis in newborn cry diagnostic systems.

Dealing with newborns' health is a delicate matter since they cannot express needs, and crying does not reflect their condition. Although newborn cries have been studied for various purposes, there is no prior research on distinguishing a certain pathology from other pathologies so far. Here, an unsophisticated framework is proposed for the study of septic newborns amid a collective of other pathologies.

Infant Cry Signal Diagnostic System Using Deep Learning and Fused Features.

Early diagnosis of medical conditions in infants is crucial for ensuring timely and effective treatment. However, infants are unable to verbalize their symptoms, making it difficult for healthcare professionals to accurately diagnose their conditions. Crying is often the only way for infants to communicate their needs and discomfort.

Using CCA-Fused Cepstral Features in a Deep Learning-Based Cry Diagnostic System for Detecting an Ensemble of Pathologies in Newborns.

Crying is one of the means of communication for a newborn. Newborn cry signals convey precious information about the newborn's health condition and their emotions. In this study, cry signals of healthy and pathologic newborns were analyzed for the purpose of developing an automatic, non-invasive, and comprehensive Newborn Cry Diagnostic System (NCDS) that identifies pathologic newborns from healthy infants.

Newborn Cry-Based Diagnostic System to Distinguish between Sepsis and Respiratory Distress Syndrome Using Combined Acoustic Features.

Crying is the only means of communication for a newborn baby with its surrounding environment, but it also provides significant information about the newborn's health, emotions, and needs. The cries of newborn babies have long been known as a biomarker for the diagnosis of pathologies. However, to the best of our knowledge, exploring the discrimination of two pathology groups by means of cry signals is unprecedented.

An Entropy-Based Architecture for Detection of Sepsis in Newborn Cry Diagnostic Systems.

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.

Nonlinear Statistical Analysis of Normal and Pathological Infant Cry Signals in Cepstrum Domain by Multifractal Wavelet Leaders.

Multifractal behavior in the cepstrum representation of healthy and unhealthy infant cry signals is examined by means of wavelet leaders and compared using the Student -test. The empirical results show that both expiration and inspiration signals exhibit clear evidence of multifractal properties under healthy and unhealthy conditions. In addition, expiration and inspiration signals exhibit more complexity under healthy conditions than under unhealthy conditions.

Machine Learning-Based Cry Diagnostic System for Identifying Septic Newborns.

Background And Objective: Processing the newborns' cry audio signal (CAS) provides valuable information about the newborns' condition. This information can be used to diagnose the disease. This article analyzes the CASs of newborns under two months old using machine learning approaches to develop an automatic diagnostic system for identifying septic infants from healthy ones.

Identification of Diseases in Newborns Using Advanced Acoustic Features of Cry Signals.

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) and resonance frequencies dysregulation (RFs); 2) conventional features such as mel-frequency cestrum coefficients.

A fully automated approach for baby cry signal segmentation and boundary detection of expiratory and inspiratory episodes.

The detection of cry sounds is generally an important pre-processing step for various applications involving cry analysis such as diagnostic systems, electronic monitoring systems, emotion detection, and robotics for baby caregivers. Given its complexity, an automatic cry segmentation system is a rather challenging topic. In this paper, a framework for automatic cry sound segmentation for application in a cry-based diagnostic system has been proposed.

Expiratory and Inspiratory Cries Detection Using Different Signals' Decomposition Techniques.

This paper addresses the problem of automatic cry signal segmentation for the purposes of infant cry analysis. The main goal is to automatically detect expiratory and inspiratory phases from recorded cry signals. The approach used in this paper is made up of three stages: signal decomposition, features extraction, and classification.

Cry-based infant pathology classification using GMMs.

Traditional studies of infant cry signals focus more on non-pathology-based classification of infants. In this paper, we introduce a noninvasive health care system that performs acoustic analysis of unclean noisy infant cry signals to extract and measure certain cry characteristics quantitatively and classify healthy and sick newborn infants according to only their cries. In the conduct of this newborn cry-based diagnostic system, the dynamic MFCC features along with static Mel-Frequency Cepstral Coefficients (MFCCs) are selected and extracted for both expiratory and inspiratory cry vocalizations to produce a discriminative and informative feature vector.

Resonance frequencies behavior in pathologic cries of newborns.

Objective: A new approach to the automatic quantification of the acoustic parameters of the cries of healthy newborns and newborns with pathologies is presented. The purpose of the present study was to examine the relationship between acoustic parameters and pathologies of interest to characterize healthy and pathologic cries of newborns.

Methods: Using MATLAB, this study included automatic estimation of F0, RF1, RF2, percentage and tuning duration, transition duration, RF2 slope, and RF1:RF2 ratio.