Open AccessIntroducing the hybrid “K-means, RLS” learning for the RBF network in obstructive apnea disease detection using Dual-tree complex wavelet transform based features
Author(s) -
Javad Ostadieh,
Mehdi Chehel Amirani
Publication year - 2020
Publication title -
journal of electrical bioimpedance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.292
H-Index - 16
ISSN - 1891-5469
DOI - 10.2478/joeb-2020-0002
Subject(s) - feature selection , pattern recognition (psychology) , artificial intelligence , computer science , support vector machine , feature extraction , obstructive sleep apnea , complex wavelet transform , wavelet , artificial neural network , sleep apnea , feature (linguistics) , wavelet transform , discrete wavelet transform , medicine , cardiology , linguistics , philosophy
Apnea is one of the deadliest diseases that can be prevented and cured if it is detected in time. In this paper, we propose a precise method for early detection of the obstructive sleep apnea (OSA) disease using the latest feature selection and extraction methods. The feature selection in this paper is based on the Dual tree complex wavelet (DT-CWT) coefficients of the ECG signals of several patients. The feature extraction from these coefficients is done using frequency and time techniques. The Feature selection is done using the spectral regression discriminant analysis (SRDA) algorithm and the classification is performed using the hybrid RBF network. A hybrid RBF neural network is introduced in this paper for detecting apnea that is much less computationally demanding than the previously presented SVM networks. Our findings showed a 3 percent improvement in the detection and at least a 30 percent reduction in the computational complexity in comparison with methods that have been presented recently.