Selection of Wavelet Transform and Neural Network Parameters for Classification of Breathing Patterns of Bio-radiolocation Signals
A novel method for classification of breathing patterns of bio-radiolocation signals breathing patterns (BSBP) in the task of non-contact screening of sleep apnea syndrome (SAS) is proposed, implemented on the base of wavelet transform (WT) and neural network (NNW) application with automated selection of their optimal parameters. The effectiveness of the proposed approach is tested on clinically verified database of BRL signals corresponding to the three classes of breathing patterns: obstructive sleep apnea (OSA); central sleep apnea (CSA); normal calm sleeping (NCS) without sleep- disordered breathing (SDB) episodes.
Keywordsbio-radiolocation non-stationary signal processing pattern recognition wavelet transform neural networks sleep apnea syndrome
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