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

Javad Ostadieh, Mehdi Chehel Amirani, Morteza Valizadeh

DOI: 10.4103/jmss.JMSS_69_19

Abstract


Background: The obstructive sleep apnea (OSA) detection has become a hot research topic because of the high risk of this disease. Aims and Objectives: 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 (DTCWT) 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: 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

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References


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