An Effective Photoplethysmography Signal Processing System Based on EEMD Method

Abstract

This study proposed an effective signal processing system based on Ensemble Empirical Mode Decomposition (EEMD) method for the analysis of Photoplethysmography (PPG). The whole system was implemented on an ARM-based SoC development platform to attain the on-line non-stationary signal processing. A non-invasive near-infrared light sensing device was used to record the continuous PPG as the input signal. According to the non-stationary characteristics of PPG, EEMD is useful to achieve accurate analysis for PPG. The signal was decomposed into several Intrinsic Mode Functions (IMFs) by EEMD. The results showed that the proposed EEMD processor can effectively solve the mode mixing problem of Empirical Mode Decomposition (EMD). This study examined its possibility based on specific architecture with an on-board Xilinx FPGA. It was helpful for non-stationary biomedical signal processing and cardiovascular diseases research.