Wavelet Variance Maximization: A contactless respiration rate estimation method based on remote photoplethysmography

Respiration is an important parameter in critical and pediatric care since its monitoring allows medical staff to detect many life-threatening diseases. One of the existing monitoring methods is based on remote photoplethysmography (rPPG). This technique consists of extracting a signal related to blood volume variations using a camera. This signal carries useful physiological information such as cardiac and respiratory rates. However, the quality of the signal is lower than regular contact-based methods and represents a major weakness of the rPPG method. In this paper, we propose an algorithm to explicitly maximize the respiratory signal quality by maximizing the Signal-to-Noise Ratio (SNR). Instead of using the regular Fast-Fourier-based energy ratio for the signal-to-noise-ratio estimation, we propose to use the continuous wavelet transform to deal with non-stationarities of the respiratory signal. The method, named Wavelet Variance Maximization (WVM), is based on the Generalized Eigen Value Decomposition (GEVD) algorithm and estimates the optimal combination of the temporal color traces to obtain a high-quality rPPG signal. Our method was tested on 12 healthy adult volunteers and the results confirm that the estimated signal has better quality than existing methods, with approximately 20% reduction of error compared to the best tested state-of-the-art method.