This paper proposes an approach to the classification of respiration states based on a neural network model by visualizing respiratory signals using a spectrogram. The analysis and processing of human biosignals are still considered some of the most crucial and fundamental research areas in both signal processing and medical applications. Recently, learning-based algorithms in signal and image processing for medical applications have shown significant improvement from both quantitative and qualitative perspectives. Human respiration is still considered an important factor for diagnosis, and it plays a key role in preventing fatal diseases in practice. This paper chiefly deals with a contactless-based approach for the acquisition of respiration data using an ultra-wideband (UWB) radar sensor because it is simple and easy for use in an experimental setup and shows high accuracy in distance estimation. This paper proposes the classification of respiratory states by using a feature visualization scheme, a spectrogram, and a neural network model. The proposed method shows competitive and promising results in the classification of respiratory states. The experimental results also show that the method provides better accuracy (precision: 0.86 and specificity: 0.90) than conventional methods that use expensive equipment for respiration measurement.

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Sleep apnea is considered one of the most critical problems of human health, and it is also considered one of the most important bio-signals in the area of medicine. In this paper, we propose the approach to detection and classification of respiratory status based on cross correlation between normal respiration and apnea, and on the characteristics of respiratory signals. The characteristics of the signals are extracted by analyzing frequency analysis. The proposed method is simple and straightforward so that it can be workable in practice. To substantiate the proposed algorithm, the experimental results are provided.