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A novel method for recognition of breathing patterns of bioradiolocation signals breathing patterns (BSBP) in the task of noncontact screening of sleep apnea syndrome (SAS) is proposed and implemented on the base of wavelet transform (WT) and neural network (NNW) applications. Selection of the optimal parameters of WT includes determination of the proper level of wavelet decomposition and the best basis for feature extraction using modified entropy criterion. Selection of the optimal properties of NNW includes defining the best number of hidden neurons and learning algorithm for the chosen NNW topology. 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.

Comparison of bioradiolocation and standard respiratory plethysmography signals during simultaneous registration of different types of the human breathing movements is performed in both time and frequency domains. For all couples of synchronized signals corresponding to bioradiolocation and respiratory plethysmography methods, the cross-correlation and spectral functions are calculated, and estimates of their generalized characteristics are defined. The obtained results consider bioradiolocation to be a reliable remote sensing technique for noncontact monitoring of breathing pattern in medical applications.

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