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Analysis of Grunting Sound in Infants for Predicting the Severity of Respiratory Distress Syndrome
Mehmet Satar1, Çağlar Cengizler2, Mustafa Özdemir1
1Faculty of Medicine, Department of Neonatology, Çukurova University, Adana, Turkey.
Insights
Computer analysis of infant grunting sounds offers an objective method for assessing respiratory distress severity. The harmonic ratio of these sounds shows promise for automated prediction and decision-making in neonatal care.
Area of Science:
- Neonatal Medicine
- Bioacoustics
- Computational Health
Background:
- Infant vocalizations, especially grunting, are crucial for assessing respiratory distress in neonatal intensive care units.
- Current methods rely on subjective auditory evaluation, which can be error-prone.
- Objective assessment of respiratory distress severity is needed.
Purpose of the Study:
- To investigate the potential of computer-aided analysis for objective assessment of respiratory tract problems in infants.
- To explore the relationship between spectral characteristics of infant grunting sounds and respiratory distress severity.
Main Methods:
- Collected 189 grunting sound segments from 38 infants.
- Analyzed spectral characteristics of the recordings.
- Correlated spectral features with respiratory distress severity and hospital stay duration.
Main Results:
- Identified three spectral features related to hospital stay duration and respiratory distress.
- The harmonic ratio was identified as the most significant spectral feature for characterizing severity.
- A promising correlation was found between expert scoring and the harmonic ratio.
Conclusions:
- Computer-aided analysis offers a potential objective grading system for infant respiratory distress, replacing subjective auditory assessment.
- The spectral characteristics of grunting sounds, particularly the harmonic ratio, can objectively reflect respiratory conditions.
- Automated prediction and decision-making in neonatal care can be enhanced using spectral features of digital grunting recordings.
Objective:
Vocalizations from infants, particularly sounds associated with respiratory distress, are fundamental for observational scoring of respiratory tract issues. Listening to these infant sounds is a prevalent technique for decision-making in newborn intensive care units. Expiratory grunting, indicative of the severity and presence of potential conditions, is valuable, however, this evaluative method is subjective and prone to error. This study investigates the potential of computer-aided analysis to offer an objective scale for assessing the severity of respiratory tract problems, utilizing digital recordings of grunting sounds.
Methods:
The original data set is formed with a total of 189 grunting sound segments collected from 38 infants. Multiple evaluation approaches were performed to reveal the relation between spectral characteristics of the recordings and the severity or existence of respiratory distress.
Results:
Three spectral features were evaluated as prominently related to hospital stay duration and respiratory distress. The harmonic ratio of the recordings was graded as the most-related spectral feature that would characterize the severity.
Conclusions:
The potential of an innovative and objective grading approach is first investigated for replacing the human ear with a computer-aided evaluation system. The results are promising and the detected relation between expert ear-based scoring and harmonic ratio suggests that the spectral character of the grunting sounds would reflect the nature of respiratory conditions. Moreover, this study underlines those spectral features of digital grunting recordings that would be functional for automated prediction and decision-making.
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