Signals of interest can be affected by various types of noise during the acquisition of photoplethysmography data. To address this problem, a denoising method based on the combination of Ensemble Empirical Mode Decomposition (EEMD) and wavelet threshold was proposed to reduce the noise associated with photoplethysmography signals. In this investigation, this approach was compared with EMD combined with wavelet denoising. Initially, an algorithm applied EEMD to the signal, which was decomposed into a limited number of Intrinsic Mode Functions (IMF). Then, it performed a correlation calculation on the components, followed by wavelet threshold denoising on the noise-containing components. Finally, the signal was reconstructed. The original signal was measured using the stm32 platform with a MAX30100 sensor. The experimental results show that the method can effectively remove high-frequency noise and baseline drift in photoplethysmography. After noise reduction, the signal-to-noise ratio is 34.09 and the root mean square error is 1.99, which improved the signal quality. This new approach facilitates accurate monitoring of photoelectric volume pulse wave signals.