Type-2 fuzzy sets applied to pattern matching for the classification of cries of infants under neurological risk

Authors:
Karen Santiago-Sánchez;Carlos A. Reyes-García;Pilar Gómez-Gil
Affiliations:
National Institute of Astrophysics, Optics and Electronics, Computer Science Department, Tonantzintla, México;National Institute of Astrophysics, Optics and Electronics, Computer Science Department, Tonantzintla, México;National Institute of Astrophysics, Optics and Electronics, Computer Science Department, Tonantzintla, México
Venue:
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

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Abstract

Crying is an acoustic event that contains information about the functioning of the central nervous system, and the analysis of the infant's crying can be a support in the distinguishing diagnosis in cases like asphyxia and hyperbilirrubinemia. The classification of baby cry has been intended by the use of different types of neural networks and other recognition approaches. In this work we present a pattern classification algorithm based on fuzzy logic Type 2 with which the classification of infant cry is realized. Experiments as well as results are also shown.