Identification of Patients with Obstructive Sleep Apnea Using Wavelets Packets and Artificial Neural Networks

DOI: 10.5176/978-981-08-7656-2ATAI2010-47

Authors: Abdulnasir Hossen

Abstract:

A new identification method for identification of patients with obstructive sleep apnea (OSA) from normal controls is investigated in this paper using estimated spectral analysis of RRI data with wavelets packets and artificial neural networks. Two sets of data are used in this paper. The training data is obtained from Sultan Qaboos University hospital while the test data is obtained from MIT databases.The training data set consists of 15 OSA and 15 normal subjects. The test data set is divided into two test sets each consists of 20 OSA and 10 normal subjects. The spectral analysis of RRI data obtained using 8 different sub-bands from wavelets packets is used as a classification feature. A simple artificial neural network of the type feed-forwardback-propagation is used for the classification task. Different types of wavelets are used to test the consistency of the approach. The accuracy of classification approaches 92.7{6e6090cdd558c53a8bc18225ef4499fead9160abd3419ad4f137e902b483c465} using a large size of data simulated with power spectral density values of the main 8 sub-bands within the mean value plus/minus the standard deviation of the power spectral density values of the original test data sets.

Keywords: Obstructive Sleep Apnea (OSA), Wavelet Packets,Spectral Analysis, Artificial Neural networks (ANN)