Classification of biomedical signals using a Haar 4 wavelet transform and a hamming neural network

Abstract

This contribution consists on the application of a hybrid technique of signals digital processing and artificial intelligence, to classify two kinds of biomedical spectra, normal brain and meningioma tumor. Each signal is processed to extract the relevant information within the range of interest. Then, a Haar 4 wavelet transform is applied to reduce the size of the spectrum without loosing its main features. This signal approximation is coded in a binary set which keeps the frequencies that could have representative amplitude peaks of each signal. The coding is input in a recursive Hamming neural network previously trained, which is able to classify it by comparing it with patterns. The results of the classification are shown for a group of signals that corresponds to human brain tissue. The advantages and disadvantages of the implemented method are discussed. © Springer-Verlag Berlin Heidelberg 2007.