Biosignals and Minimally Invasive Technologies (BioMIT.org), Electronic Engineering Department, Universitat Politecnica de Valencia, 46022 Valencia, Spain.
Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, 16071 Cuenca, Spain.
Sensors (Basel). 2023 Dec 26;24(1):141. doi: 10.3390/s24010141.
Health-tracking from photoplethysmography (PPG) signals is significantly hindered by motion artifacts (MAs). Although many algorithms exist to detect MAs, the corrupted signal often remains unexploited. This work introduces a novel method able to reconstruct noisy PPGs and facilitate uninterrupted health monitoring. The algorithm starts with spectral-based MA detection, followed by signal reconstruction by using the morphological and heart-rate variability information from the clean segments adjacent to noise. The algorithm was tested on (a) 30 noisy PPGs of a maximum 20 s noise duration and (b) 28 originally clean PPGs, after noise addition (2-120 s) (1) with and (2) without cancellation of the corresponding clean segment. Sampling frequency was 250 Hz after resampling. Noise detection was evaluated by means of accuracy, sensitivity, and specificity. For the evaluation of signal reconstruction, the heart-rate (HR) was compared via Pearson correlation (PC) and absolute error (a) between ECGs and reconstructed PPGs and (b) between original and reconstructed PPGs. Bland-Altman (BA) analysis for the differences in HR estimation on original and reconstructed segments of (b) was also performed. Noise detection accuracy was 90.91% for (a) and 99.38-100% for (b). For the PPG reconstruction, HR showed 99.31% correlation in (a) and >90% for all noise lengths in (b). Mean absolute error was 1.59 bpm for (a) and 1.26-1.82 bpm for (b). BA analysis indicated that, in most cases, 90% or more of the recordings fall within the confidence interval, regardless of the noise length. Optimal performance is achieved even for signals of noise up to 2 min, allowing for the utilization and further analysis of recordings that would otherwise be discarded. Thereby, the algorithm can be implemented in monitoring devices, assisting in uninterrupted health-tracking.
基于光电容积脉搏波（PPG）信号的健康追踪受到运动伪影（MA）的严重阻碍。尽管存在许多检测 MA 的算法，但受污染的信号通常仍未被充分利用。本研究提出了一种新颖的方法，能够重建噪声 PPG 并促进不间断的健康监测。该算法首先基于光谱进行 MA 检测，然后使用相邻干净段的形态和心率变异性信息对噪声进行信号重建。该算法在（a）最长 20 秒噪声持续时间的 30 个噪声 PPG 以及（b）在添加（2-120 秒）（1）和（2）没有取消相应干净段的情况下原本干净的 28 个 PPG 上进行了测试。重采样后的采样频率为 250 Hz。噪声检测通过准确性、灵敏度和特异性进行评估。对于信号重建的评估，通过心电图（ECG）和重建的 PPG 之间的心率（HR）比较，使用 Pearson 相关系数（PC）和绝对误差（a），以及（b）原始和重建的 PPG 之间的 HR 比较。还对（b）中原始和重建段的 HR 估计的差异进行了 Bland-Altman（BA）分析。对于（a）中的噪声检测准确率为 90.91%，对于（b）中的准确率为 99.38-100%。对于 PPG 重建，（a）中的 HR 相关性为 99.31%，在（b）中所有噪声长度的相关性均大于 90%。平均绝对误差为（a）中的 1.59 bpm 和（b）中的 1.26-1.82 bpm。BA 分析表明，在大多数情况下，超过 90%的记录落在置信区间内，无论噪声长度如何。即使对于长达 2 分钟的信号，也能实现最佳性能，从而允许利用和进一步分析否则会被丢弃的记录。因此，该算法可以在监测设备中实现，有助于不间断的健康追踪。
