Feng Jingheng, Ling Bingo Wing-Kuen
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China.
Sensors (Basel). 2025 Jun 15;25(12):3746. doi: 10.3390/s25123746.
For four-channel photoplethysmograms (PPGs), this paper employs quaternion-valued medians as features for performing non-invasive blood glucose estimation. However, as the PPGs are contaminated by noise, the quaternion-valued medians are also contaminated by noise. To address this issue, principal component analysis (PCA) is employed for performing the denoising. In particular, the covariance matrix of the four-channel PPGs is computed and the eigen vectors of the covariance matrix are found. Then, the quaternion-valued medians of the four-channel PPGs are found and these quaternion-valued medians are represented as the four-channel real-valued vectors. By applying the PCA to these four-channel real-valued vectors and reconstructing the denoised four-dimensional real-valued vectors, these four-dimensional real-valued vectors are denoised. Next, these denoised four-dimensional real-valued vectors are represented as the denoised quaternion-valued medians. Compared to the traditional denoising methods and the traditional feature extraction methods that are performed in the individual channels, the quaternion-valued medians and the PCA are computed via fusing all of these four-channel PPGs together. Hence, the hidden relationships among these four channels of the PPGs are exploited. Finally, the random forest is used to estimate the blood glucose levels (BGLs). Our proposed PCA-based quaternion-valued medians are compared to the median of each channel of the PPGs and other features such as the time-domain features and the frequency-domain features. Here, the effectiveness and robustness of our proposed method is demonstrated using two datasets. The computer numerical simulation results indicate that our proposed PCA-based quaternion-valued medians outperform the existing quaternion-valued medians and the other features for performing non-invasive blood glucose estimation.
对于四通道光电容积脉搏波图（PPG），本文采用四元数中值作为特征来进行无创血糖估计。然而，由于PPG受到噪声污染，四元数中值也被噪声污染。为了解决这个问题，采用主成分分析（PCA）进行去噪。具体而言，计算四通道PPG的协方差矩阵并找到该协方差矩阵的特征向量。然后，找到四通道PPG的四元数中值，并将这些四元数中值表示为四通道实值向量。通过将PCA应用于这些四通道实值向量并重建去噪后的四维实值向量，对这些四维实值向量进行去噪。接下来，将这些去噪后的四维实值向量表示为去噪后的四元数中值。与在各个通道中执行的传统去噪方法和传统特征提取方法相比，四元数中值和PCA是通过将所有这些四通道PPG融合在一起进行计算的。因此，利用了PPG这四个通道之间的隐藏关系。最后，使用随机森林来估计血糖水平（BGL）。将我们提出的基于PCA的四元数中值与PPG每个通道的中值以及其他特征（如时域特征和频域特征）进行比较。在此，使用两个数据集证明了我们提出的方法的有效性和鲁棒性。计算机数值模拟结果表明，我们提出的基于PCA的四元数中值在进行无创血糖估计方面优于现有的四元数中值和其他特征。
