Joint discrete wavelet transform and improved Savitzky-Golay filtering for noise reduction of PPG signals

Abstract

The Photoplethysmography (PPG) stands as a fundamental measurement in wearable devices for human physiological parameters. This signal is susceptible to interference from external environmental factors during measurement of human physiological parameters. Prior research has addressed this issue predominantly through discrete wavelet transform (DWT) based approaches, yielding some success. However, the signals processed by these methods are often not smooth enough or have distortions, subsequently undermining confidence in the accuracy of derived physiological parameters. To mitigate this issue, this paper introduces a novel algorithm integrating DWT with Savitzky-Golay (SG) filtering to reduce noise on PPG signals. Specifically, the algorithm initially employs DWT on the original PPG signal for signal decomposition, and the preliminary denoising signals are subsequently obtained using soft thresholding on these decomposed signals. After that, a grid search algorithm is adopted to optimize the parameters of the SG filter, aiming to realize the smoothing of PPG signals. The final denoising PPG signal can be obtained from the output port of the SG filter. Finally, the algorithm combining DWT and MA filtering is compared with our proposed algorithm, and the experimental results show that the proposed method not only effectively reduces the noise interference, but also preserves the original features of the signal, which verifies the effectiveness and advancement of the proposed algorithm.