A de-noising method for photoplethysmographic signals based on CoatiOA-VMD-WT

In the process of collecting photoplethysmographic (PPG) signals, it is particularly susceptible to noise from the environment and the object being collected, which can cause a large gap between the collected signal and the true value. In response to this problem, a method for denoising PPG signals is proposed in this paper. The paper presents a long-nosed otter optimization algorithm that uses the minimum envelope entropy as a criterion to determine the optimal number of modal components and the punishment factor for the variational mode decomposition (VMD). These two parameters directly affect the quality of the VMD processing results. The original signal is decomposed using VMD, the IMFs are distinguished using cosine similarity, and then the signal is denoised using wavelet thresholding. Finally, the signal is reconstructed to obtain the denoised signal. The simulation and experimental results show that compared with traditional denoising methods, the method proposed in this paper has higher signal-to-noise ratio, lower mean square deviation, and higher waveform similarity.