Abstract Photoplethaysmography (PPG) is a noninvasive technique that reflects blood volume changes in the vessel and plays a vital role in the diagnosis of cardiovascular diseases. Nevertheless, it is extremely susceptive to motion artifact (MA) caused by the objects’ intentional or unintentional movements and then affects the accuracy of the diagnosis results. To solve this problem, this paper employed the gradient projection for sparse recovery (GPSR) algorithm based on the compressed sensing (CS) theoretical framework. For the non-sparse signals, the CS theory mainly consist two steps: sparse and recovery. First, in order to ensure the significant information not be destroyed, the Haar wavelet base was used to find the best sparse field for sparse the signals. After that the MA was removed from the contaminated PPG signals while the sparse signals were recovered. During the experiment, three types of noise were measured, and then GPSR algorithm was used to process the noisy signals, results showed that the GPSR algorithm significantly reduced the movement interference in the signals. In order to make the conclusions more convincing, other 50 healthy adults with vertical movement of MA were measured, the heart rate and mean square error (MSE) of the original and reconstruct PPG signals were calculated. Comparing the obtained heart rates with the noiseless PPG signals’ heart rates through the Bland-Altman analysis method, it was shown that the range of the differences between the noisy and noiseless PPG signals were about ±23 beat/min, and the reconstructed PPG signals were about ±2.7 beat/min. The Box Plot chart indicated that the MSE of the reconstructed signals was reduced about 50% compared with that of the noisy signals.
Hu Kaili,Jin Jie,Zhang Ruifeng. Research on Gradient Projection for Sparse Recovery Algorithm in PPG Signals Reconstruction[J]. Chinese Journal of Biomedical Engineering, 2017, 36(5): 536-542.
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