title: Application of Pattern Recognition Techniques to the Classification of Full-Term and Preterm Infant Cry.
abstract: OBJECTIVE
  Scientific and clinical advances in perinatology and neonatology have enhanced the chances of survival of preterm and very low weight neonates. Infant cry analysis is a suitable noninvasive complementary tool to assess the neurologic state of infants particularly important in the case of preterm neonates. This article aims at exploiting differences between full-term and preterm infant cry with robust automatic acoustical analysis and data mining techniques.
  
  
  METHODS
  Twenty-two acoustical parameters are estimated in more than 3000 cry units from cry recordings of 28 full-term and 10 preterm newborns.
  
  
  METHODS
  Feature extraction is performed through the BioVoice dedicated software tool, developed at the Biomedical Engineering Lab, University of Firenze, Italy. Classification and pattern recognition is based on genetic algorithms for the selection of the best attributes. Training is performed comparing four classifiers: Logistic Curve, Multilayer Perceptron, Support Vector Machine, and Random Forest and three different testing options: full training set, 10-fold cross-validation, and 66% split.
  
  
  RESULTS
  Results show that the best feature set is made up by 10 parameters capable to assess differences between preterm and full-term newborns with about 87% of accuracy. Best results are obtained with the Random Forest method (receiver operating characteristic area, 0.94).
  
  
  CONCLUSIONS
  These 10 cry features might convey important additional information to assist the clinical specialist in the diagnosis and follow-up of possible delays or disorders in the neurologic development due to premature birth in this extremely vulnerable population of patients. The proposed approach is a first step toward an automatic infant cry recognition system for fast and proper identification of risk in preterm babies.
authors.affiliation: Department of Information Engineering, Università degli Studi di Firenze, Firenze, Italy.
authors.forename: Silvia
authors.initials: S
authors.lastname: Orlandi
authors.affiliation: Computer Science Department, Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Puebla, Mexico.
authors.forename: Carlos Alberto
authors.initials: CA
authors.lastname: Reyes Garcia
authors.affiliation: Department of Information Engineering, Università degli Studi di Firenze, Firenze, Italy; Department of Electrical, Electronic and Information Engineering (DEI) "Guglielmo Marconi", University of Bologna, Bologna, Italy.
authors.forename: Andrea
authors.initials: A
authors.lastname: Bandini
authors.affiliation: Department of Neuroscience, Psychology, Drug Research and Child Health, Università degli Studi di Firenze, Firenze, Italy.
authors.forename: Gianpaolo
authors.initials: G
authors.lastname: Donzelli
authors.affiliation: Department of Information Engineering, Università degli Studi di Firenze, Firenze, Italy. Electronic address: claudia.manfredi@unifi.it.
authors.forename: Claudia
authors.initials: C
authors.lastname: Manfredi
mesh_terms: D000162:Acoustics; D003448:Crying; D057225:Data Mining; D005260:Female; D005865:Gestational Age; D006801:Humans; D018730:Infant Behavior; D007231:Infant, Newborn; D007234:Infant, Premature; D016015:Logistic Models; D008297:Male; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012984:Software; D013018:Sound Spectrography; D060388:Support Vector Machine; D047929:Term Birth
keywords: Acoustical parameters; Automatic classification; Feature selection; Infant cry analysis; Preterm newborn