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International Journal of Health Professions

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Comparison of Supervised-learning Models for Infant Cry Classification / Vergleich von Klassifikationsmodellen zur Säuglingsschreianalyse

Tanja Fuhr / Henning Reetz
  • Goethe Universität Frankfurt am Main, Institut für Phonetik, Senckenberganlage 31, 60325 Frankfurt, GERMANY
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Carla Wegener
  • Hochschule Fresenius, Fachbereich Gesundheit & Soziales, Limburger Str. 2, 65510 Idstein, GERMANY
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2015-05-29 | DOI: https://doi.org/10.1515/ijhp-2015-0005

Abstract

Cries of infants can be seen as an indicator for several developmental diseases. Different types of classification algorithms have been used in the past to classify infant cries of healthy infants and those with developmental diseases. To determine the ability of classification models to discriminate between healthy infant cries and various cries of infants suffering from several diseases, a literature search for infant cry classification models was performed; 9 classification models were identified that have been used for infant cry classification in the past. These classification models, as well as 3 new approaches were applied to a reference dataset containing cries of healthy infants and cries of infants suffering from laryngomalacia, cleft lip and palate, hearing impairment, asphyxia and brain damage. Classification models were evaluated according to a rating schema, considering the aspects accuracy, degree of overfitting and conformability. Results indicate that many models have issues with accuracy and conformability. However, some of the models, like C5.0 decision trees and J48 classification trees provide promising results in infant cry classification for diagnostic purpose.

Abstract

Verschiedene Klassifikationsverfahren konnten bereits zeigen, dass es möglich ist, zwischen gesunden Säuglingsschreien und pathologischen Säuglingsschreien zu unterscheiden. Bislang fehlte jedoch ein systematischer Vergleich der verschiedenen Ansätze. Für diesen Artikel wurden in einer systematischen Literatursuche 9 Klassifikationsmodelle identifiziert, die bereits in der Säuglingsschreiforschung genutzt wurden. Zusammen mit drei weiteren, bislang ungenutzten Ansätzen, wurden die Schreie von gesunden Säuglingen sowie von Säuglingen mit Laryngomalazie, Lippen-Kiefer-Gaumenspalte, Hörstörung, Sauerstoffmangel und Hirnschädigung anhand ihrer akustischen Parameter klassifiziert. Die Leistungsfähigkeit aller Modelle wurde mittels eines standardisierten Schemas nach Genauigkeit, Überanpassung an den Trainingsdatensatz und Nachvollziehbarkeit des Verfahrens bewertet und verglichen. Die Ergebnisse zeigen, dass einige der Modelle Schwächen in der Genauigkeit und Nachvollziehbarkeit aufweisen. Jedoch erzielen Modelle wie die C5.0 und J48 Entscheidungsbäume vielversprechende Ergebnisse, die das Erkennen des jeweiligen Störungsbildes am Schrei mit einer hohen Genauigkeit ermöglichen.

Keywords : supervised-learning models - infant cry - developmental disorders - classification

Keywords : Klassifkationsmodelle - Säuglingsschrei - Entwicklungsstörung

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About the article

Received: 2014-11-14

Accepted: 2015-01-22

Published Online: 2015-05-29

Published in Print: 2015-06-01


Citation Information: International Journal of Health Professions, Volume 2, Issue 1, Pages 4–15, ISSN (Online) 2296-990X, DOI: https://doi.org/10.1515/ijhp-2015-0005.

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[1]
Lizbeth Peralta-Malváez, Omar López-Rincón, David Rojas-Velázquez, Luis Oswaldo Valencia-Rosado, Roberto Rosas-Romero, Gibran Etcheverry, David Pinto, Vivek Kumar Singh, Aline Villavicencio, Philipp Mayr-Schlegel, and Efstathios Stamatatos
Journal of Intelligent & Fuzzy Systems, 2018, Volume 34, Number 5, Page 3281
[2]
J. Saraswathy, M. Hariharan, Wan Khairunizam, J. Sarojini, N. Thiyagar, Y. Sazali, and Shafriza Nisha
Biocybernetics and Biomedical Engineering, 2018

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