Breathing Pattern Assessment Through the Empirical Mode Decomposition and the Empirical Wavelet Transform Algorithms

Abstract

In healthcare applications, the photoplethysmography concept is widely employed to estimate the heart rate, breathing rate, and other vital signs of humans, by using facial videos. However, the latter physiological indicators are fundamentally used to determine the physiological and pathological state of a person. In consequence, the present study will notably concentrate on the respiratory rate value extraction through a non-contact technique. The current approach is mainly based on image processing for PPG data gathering, and plethysmography (PPG) signal normalization, signal decomposition, and Fourier transform in order to extract the adequate candidate for breathing rate estimation. Moreover, our study contains a result comparison of the decomposition eminent methods such as the Empirical Mode Decomposition (EMD) and Empirical Wavelet Transform (EWT), the metrological interpretation of the results achieved from the experiment’s research is discussed and conclusions are presented.