Obstructive Sleep Apnea Diagnosis Using Mean Coat Clustering Algorithm and Wavelet Transform
Subject Areas : Electrical Engineering
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Keywords: obstructive sleep apneaT, symptom discount, hybrid recursive least squares okay-manner, multi cluster symptom choice,
Abstract :
The detection of obstructive sleep apnea (OSA) has turn out to be a warm studies topic because of the excessive danger of this sickness. in this paper, we tested a few effective and low-price computational sign processing techniques for this undertaking and compared their effects with current achievements in OSA detection.
dual-tree complex wavelet transform (DT-CWT) is used in this paper to extract feature coefficients. 8 nonlinear features are extracted from those coefficients after which decreased the usage of a multi-cluster characteristic selection (MCFS) algorithm. The remaining functions are implemented to a hybrid "ok-approach, RLS" RBF network, which is a small computational rival for the support vector device (SVM) own family of networks.
The results confirmed a appropriate OSA detection percentage near 96% with a discounted complexity of virtually one-third of previously provided SVM-primarily based techniques.
symptom discount, hybrid recursive least squares okay-manner, multi cluster symptom choice, obstructive sleep apnea
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