Continuous monitoring of tissue oxygen metabolism based on multi-wavelength diffuse correlation spectroscopy using LSTM-based RNN model

Abstract

Growing incidences of cardiovascular diseases have driven demand for continuous tissue oxygen metabolism monitoring. Diffuse optical spectroscopy (DOS) or near-infrared spectroscopy (NIRS) is typically employed to measure oxygenated and de-oxygenated hemoglobin, while diffuse correlation spectroscopy (DCS) provides a direct measure of tissue blood flow noninvasively. Traditionally, combined DCS and DOS/NIRS approaches are utilized for monitoring oxygen metabolism, which needs two different numerical models to calculate blood flow and oxygen saturation. In this paper, we evaluate the utility of multi-wavelength diffuse correlation spectroscopy (mwDCS) using a proposed deep learning approach, i.e., long short-term memory (LSTM) based recurrent neural network (RNN). Arterial occlusion experiments were performed in vivo to generate the data from 6 healthy individuals. Further, based on the dataset acquired by mwDCS, a LSTM-based RNN model was developed to directly assess changes of blood flow and oxygen saturation. Compared to conventional methods, the in vivo experiments in this study demonstrated a more portable system with promising results. Pearsons correlation coefficients of the proposed model reached 0.82 and 0.64 for the changes of blood flow and oxygen saturation, respectively. Our results shows that mwDCS using LSTM-based RNN model provides an alternative method for continuous tissue oxygen metabolism monitoring.