Title :
Classification of Four Types of Common Murmurs using Wavelets and a Learning Vector Quantization Network
Author :
Rios-Gutiérrez, F. ; Alba-Flores, R. ; Ejaz, K. ; Nordehn, G. ; Andrisevic, N. ; Burns, S.
Author_Institution :
Univ. of Minnesota Duluth, Duluth
Abstract :
In this work we present the development of a system that can be used for the study, detection and classification of human heart sounds using digital signal processing and artificial intelligence techniques. The design and implementation of such system is broken down into two processes: digital signal processing part and artificial intelligence part. The ultimate goal of the project is to develop an intelligent system that can be used for the detection and classification of various types of human heart murmurs.
Keywords :
acoustic signal detection; bioacoustics; cardiology; learning (artificial intelligence); medical signal detection; signal classification; vector quantisation; wavelet transforms; artificial intelligence techniques; common murmurs; digital signal processing; human heart sounds; learning vector quantization network; wavelets; Artificial intelligence; Digital signal processing; Discrete wavelet transforms; Heart; Humans; Medical diagnostic imaging; Signal design; Signal processing algorithms; Stethoscope; Vector quantization;
Conference_Titel :
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9490-9
DOI :
10.1109/IJCNN.2006.247015