Enhancing obstructive apnea disease detection using dual-tree complex wavelet transform-based features and the hybrid “K-means, recursive least-squares” learning for the radial basis function network

Abstract

Background: The obstructive sleep apnea (OSA) detection has become a hot research topic because of the high risk of this disease. In this paper, we tested some powerful and low computational signal processing techniques for this task and compared their results with the recent achievements in OSA detection. Methods: The Dual-tree complex wavelet transform (DT-CWT) is used in this paper to extract feature coefficients. From these coefficients, eight non-linear features are extracted and then reduced by the Multi-cluster feature selection (MCFS) algorithm. The remaining features are applied to the hybrid “K-means, RLS” RBF network which is a low computational rival for the Support vector machine (SVM) networks family. Results and Conclusion: The results showed suitable OSA detection percentage near 96% with a reduced complexity of nearly one third of the previously presented SVM based methods.

Keywords

• classification
• feature reduction
• hybrid k-means recursive least-squares
• multi-cluster feature selection
• obstructive sleep apnea
• single-lead electrocardiogram