Optimization of PPG Signal Processing based on Hybrid Filtering Technology

Ruiyang Xue

doi:10.54254/2755-2721/2025.22712

Research Article

Open access

Published on 15 May 2025

Download pdf

Xue,R. (2025). Optimization of PPG Signal Processing based on Hybrid Filtering Technology. Applied and Computational Engineering,151,21-33.

Export citation

Optimization of PPG Signal Processing based on Hybrid Filtering Technology

Ruiyang Xue *^,1,

¹ Logistics Engineering College, Shanghai Maritime University, Shanghai, People Republic of China, 200120

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/2025.22712

Abstract

With the widespread adoption of wearable devices, pulse oximeters face challenges such as motion artifacts and environmental noise in dynamic scenarios, limiting their accuracy and reliability. This study focuses on optimizing a hybrid filtering framework combining low-pass filtering and LMS adaptive filtering to enhance the performance of photoplethysmography (PPG)-based oximetry. Utilizing a publicly available dataset from Biagetti et al., which includes PPG and triaxial accelerometer data from seven subjects under rest and exercise conditions, we implemented a dynamic low-pass filter with adjustable cutoff frequencies and an LMS algorithm with motion-dependent step size adaptation Comparative experiments with wavelet denoising demonstrated that the proposed framework achieved a signal-to-noise ratio (SNR) of 61.37 dB, a mean squared error (MSE) of 0.0029, and a correlation coefficient (R) of 0.9849, significantly outperforming conventional methods. Results validate the hybrid framework’s effectiveness in suppressing noise while preserving physiological signal integrity, offering a robust solution for wearable health monitoring in dynamic environments.

Keywords

PPG signal, motion artifacts, LMS adaptive filtering, low-pass filter

View pdf

References

[1]. Allen J. (2007). Photoplethysmography and its application in clinical physiological measurement. Physiological measurement, 28(3), R1–R39. https://doi.org/10.1088/0967-3334/28/3/R01

[2]. Lee, J., Kim, M., Park, H.-K., & Kim, I. Y. (2020). Motion Artifact Reduction in Wearable Photoplethysmography Based on Multi-Channel Sensors with Multiple Wavelengths. Sensors, 20(5), 1493. https://doi.org/10.3390/s20051493

[3]. Park, J., Seok, H. S., Kim, S. S., & Shin, H. (2022). Photoplethysmogram Analysis and Applications: An Integrative Review. Frontiers in physiology, 12, 808451. https://doi.org/10.3389/fphys.2021.808451

[4]. R. Ahmed, A. Mehmood, M. M. U. Rahman and O. A. Dobre, "A Deep Learning and Fast Wavelet Transform-Based Hybrid Approach for Denoising of PPG Signals," in IEEE Sensors Letters, vol. 7, no. 7, pp. 1-4, July 2023, Art no. 6003504, doi: 10.1109/LSENS.2023.3285135.

[5]. lgendi, M., Martinelli, I. & Menon, C. Optimal signal quality index for remote photoplethysmogram sensing. npj Biosensing 1, 5 (2024). https://doi.org/10.1038/s44328-024-00002-1

[6]. M. G. Vázquez Domínguez, J. G. Ávalos Ochoa, J. C. Sánchez García and B. Becerra Luna, "Spectral coherence as a method for evaluating adaptive filtering techniques in photoplethysmography," in IEEE Latin America Transactions, vol. 23, no. 4, pp. 274-284, April 2025, doi: 10.1109/TLA.2025.10930368.

[7]. Zhao Xiaoqun & Zhang Jie. (2013). Research on the implementation method of Butterworth low-pass filter. Journal of Dalian Nationalities University, 15(01), 72-75. doi:10.13744/j.cnki.cn21-1431/g4.2013.01.019.

[8]. Zhao, Xuesong. (2022). Research on the performance of LMS adaptive filtering algorithm (Master's thesis, Harbin University of Science and Technology). Master's degree https://link.cnki.net/doi/10.27063/d.cnki.ghlgu.2022.001163doi:10.27063/d.cnki.ghlgu.2022.001163.

[9]. Pollreisz, D., TaheriNejad, N. Detection and Removal of Motion Artifacts in PPG Signals. Mobile Netw Appl 27, 728-738 (2022). https://doi.org/10.1007/s11036-019-01323-6

[10]. Biagetti, G., Crippa, P., Falaschetti, L., Saraceni, L., Tiranti, A., & Turchetti, C. (2020). Dataset from PPG wireless sensor for activity monitoring. Data in brief, 29, 105044.

[11]. Liu Peng, Lu Tancheng, Lu Yuanyuan, Deng Yongli & Lu Qiyong. (2014). Fall detection based on MEMS three-axis acceleration sensor. Journal of Sensor Technology, 27(04), 570-574.

[12]. Cao Yiqing, Li Shanming, Wang Zhuo & Shang Yiqi. (2008). Influence of low-pass filter cutoff frequency in shock acceleration peak measurement. Science and Technology Review, (16), 81-84.

[13]. Zhang Huixian. (2012). Research and application of adaptive filtering algorithm (Master's thesis, Xidian University). Masterhttps://kns.cnki.net/kcms2/article/abstract?v=vSXt4VHTcs5_VrUAk-VD_J6Eg-C6uZyEpLY_mTtwTy4EFKfWuwh_-j0fzkYstX5Agpj8qTgigjGhiGT747v8l4FEcj5g5sw3tc768VEZ-mRIFY_Ymc7kq61Q6Pf5cJK5A8ycq_mZitAnZ2lqFUTLESsZ3vA9cNbQCTQb2pkH719kVumIson7-5r0NBqkFVOb6LPYYSL1t4E=&uniplatform=NZKPT&language=CHS

[14]. L. Zhang, X. Yu, H. Xie, J. Lin and Z. Wang, "Removal of Motion Artifacts in PPG Signals Based on the CEEMDAN-MPE and VS_LMS Adaptive Filter," in IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-10, 2024, Art no. 4010610, doi: 10.1109/TIM.2024.3453341.

[15]. Yang Yuliang, Xu Lin, Zhao Lulu, Chen Yang & Wang Meng. (2013). A PPG Signal Processing Method Based on Wavelet Transform and Adaptive Filtering. (eds.) Proceedings of the 32nd Chinese Control Conference (Volume C) (pp. 325-329). College of Information Technology Science, Nankai University.

[16]. Liu Tianyi. (2022). Research and Design of a Microcontroller for Heart Rate Sensing Chips (Master's Thesis, Xidian University). Master's Degree. https://link.cnki.net/doi/10.27389/d.cnki.gxadu.2022.001594doi:10.27389/d.cnki.gxadu.2022.001594.

Cite this article

Xue,R. (2025). Optimization of PPG Signal Processing based on Hybrid Filtering Technology. Applied and Computational Engineering,151,21-33.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 3rd International Conference on Software Engineering and Machine Learning

Conference website: https://2025.confseml.org/

ISBN：978-1-80590-091-7(Print) / 978-1-80590-092-4(Online)

Conference date: 2 July 2025

Editor：Marwan Omar

Series: Applied and Computational Engineering

Volume number: Vol.151

ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).