Machine Learning-Based Cry Diagnostic System for Identifying Septic Newborns

• Published in: Journal of voice 2024-07, Vol.38 (4), p.963.e1-963.e14
• Main Authors: Matikolaie, Fatemeh Salehian, Tadj, Chakib
• Format: Article
• Language: English
• Subjects: Classifiers fusion; Decision tree; Discriminant analysis; Feature manipulation; Infants’ cry; Mel frequency cepstral coefficient; Principal component analysis; Prosodic feature; Sepsis; Support vector machine
• ISSN: 0892-1997; 1873-4588; 1873-4588
• DOI: 10.1016/j.jvoice.2021.12.021

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. Septic infants have not been studied in this context. The proposed features include Mel frequency cepstral coefficients and the prosodic features of tilt, rhythm, and intensity. The performance of each feature set was evaluated using a collection of classifiers, including Support Vector Machine (SVM), decision tree, and discriminant analysis. We also examined the majority voting method for improving the classification results and feature manipulation and multiple classifier framework, which has not previously been reported in the literature on developing an automatic diagnostic system based on the infant's CAS. We tested our methodology on two datasets of expiration and inspiration episodes of newborns' CASs. The framework of the concatenation of all feature sets using quadratic SVM resulted in the best F-score with 86% for the expiration dataset. Furthermore, the framework of tilt feature set with quadratic discriminant with 83.90% resulted in the best F-score for inspiration. We found out that septic infants cry differently than healthy infants through these experiments. Thus, our proposed method can be used as a noninvasive tool for identifying septic infants from healthy ones only based on their CAS.