Technol Health Care. 2024;32(4):2793-2814. doi: 10.3233/THC-231996.
Photoplethysmography (PPG) signals are sensitive to motion-induced interference, leading to the emergence of motion artifacts (MA) and baseline drift, which significantly affect the accuracy of PPG measurements.
The objective of our study is to effectively eliminate baseline drift and high-frequency noise from PPG signals, ensuring that the signal's critical frequency components remain within the range of 1 ∼ 10 Hz.
This paper introduces a novel hybrid denoising method for PPG signals, integrating Variational Mode Decomposition (VMD) with an improved wavelet threshold function. The method initially employs VMD to decompose PPG signals into a set of narrowband intrinsic mode function (IMF) components, effectively removing low-frequency baseline drift. Subsequently, an improved wavelet thresholding algorithm is applied to eliminate high-frequency noise, resulting in denoised PPG signals. The effectiveness of the denoising method was rigorously assessed through a comprehensive validation process. It was tested on real-world PPG measurements, PPG signals generated by the Fluke ProSim™ 8 Vital Signs Simulator with synthesized noise, and extended to the MIMIC-III waveform database.
The application of the improved threshold function let to a substantial 11.47% increase in signal-to-noise ratio (SNR) and an impressive 26.75% reduction in root mean square error (RMSE) compared to the soft threshold function. Furthermore, the hybrid denoising method improved SNR by 15.54% and reduced RMSE by 37.43% compared to the improved threshold function.
This study proposes an effective PPG denoising algorithm based on VMD and an improved wavelet threshold function, capable of simultaneously eliminating low-frequency baseline drift and high-frequency noise in PPG signals while faithfully preserving their morphological characteristics. This advancement establishes the foundation for time-domain feature extraction and model development in the domain of PPG signal analysis.
光电容积脉搏波（PPG）信号对运动引起的干扰很敏感，导致运动伪影（MA）和基线漂移的出现，这会显著影响 PPG 测量的准确性。
本研究的目的是有效地消除 PPG 信号中的基线漂移和高频噪声，确保信号的关键频率分量保持在 1∼10 Hz 的范围内。
本文介绍了一种用于 PPG 信号的新型混合去噪方法，该方法将变分模态分解（VMD）与改进的小波阈值函数相结合。该方法首先采用 VMD 将 PPG 信号分解为一组窄带固有模态函数（IMF）分量，有效地去除低频基线漂移。然后，采用改进的小波阈值算法消除高频噪声，得到去噪后的 PPG 信号。通过全面的验证过程严格评估了去噪方法的有效性。它在真实的 PPG 测量、带有合成噪声的 Fluke ProSim™ 8 生命体征模拟器生成的 PPG 信号以及扩展到 MIMIC-III 波形数据库中进行了测试。
与软阈值函数相比，改进的阈值函数的应用使信噪比（SNR）提高了 11.47%，均方根误差（RMSE）降低了 26.75%。此外，与改进的阈值函数相比，混合去噪方法使 SNR 提高了 15.54%，RMSE 降低了 37.43%。
本研究提出了一种基于 VMD 和改进的小波阈值函数的有效的 PPG 去噪算法，能够同时消除 PPG 信号中的低频基线漂移和高频噪声，同时忠实地保留其形态特征。这一进展为 PPG 信号分析领域的时域特征提取和模型开发奠定了基础。
