An effective photoplethysmography signal processing system based on EEMD method

Jia-Ju Liao, Shang-Yi Chuang, Chia-Ching Chou, Chia-Chi Chang, W. Fang
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引用次数: 4

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.
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一种有效的基于EEMD方法的光容积脉搏波信号处理系统
本研究提出了一种基于集成经验模态分解(EEMD)方法的有效信号处理系统,用于光体积脉搏波(PPG)分析。整个系统在基于arm的SoC开发平台上实现,实现了非平稳信号的在线处理。采用无创近红外光传感装置记录连续PPG作为输入信号。由于PPG的非平稳特性,EEMD有助于实现对PPG的精确分析。利用EEMD将信号分解为若干个本征模态函数(IMFs)。结果表明,所提出的EEMD处理器能够有效地解决经验模态分解(EMD)的模态混合问题。本研究考察了基于板载Xilinx FPGA的特定架构的可能性。对非平稳生物医学信号处理和心血管疾病研究有一定的帮助。
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