The growing interest in wearable computing during daily life has lead to many studies on unconstrained biological signal measurements. The photoplethysmography (PPG), as an extremely useful wearable sensing medical diagnostic tool, adequately creates a health care monitoring device since it can be easily measured in our bodies. In this paper, we study the decomposition of photoplethysmography signal based on a finite Gaussian basis. When we employ a set of n (n <8) Gaussian basis to approximate the original PPG signal, we can use a feature vector only including 3n parameters to represent the original PPG signal, with almost no losses in geometrical shape. In contrast with a thousand samples in time domain, the proposed method can save a lot of resources in processing, transmitting and storing PPG signal. Besides that, we studied the application of our decomposition method for the extraction of respiratory and heart information from PPG signal. Determination of baseline heart rate and respiratory rate were easily identified in the experiments of exercise condition. The results indicate the accurate determination of heart rate and respiratory rate from PPG signal. We believe that method could soon be easily incorporated into current Body Area Network applications. 
A new signal decomposition to estimate breathing rate and heart rate from photoplethysmography signal