In this paper, a noncontact breathing disorder recognition system has been proposed for identifying irregular breathing patterns. The proposed system consists of a Doppler radar-based sensor module and a machine-learning-based breathing disorder recognition module. A custom-designed 2.4-GHz continuous wave (CW) digital-IF Doppler radar is utilized as the radar sensor module to accurately capture the time-domain breathing waveform. Then a recognition module is designed with selected features and optimized classifiers. Four sets of experiments have been carried out to evaluate the proposed system comprehensively. In which, for the laboratorial experiments, the proposed system achieves 94.7 % classification accuracy using the linear SVM classifier with seven selected features. Results of clinical experiments demonstrate the feasibility of long-term breathing disorder recognition with good accuracy and robustness, and illustrate the potential of the proposed solution for the auxiliary diagnosis of diseases.

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