Research on dynamic blood oxygen saturation measurement based on motion noise reconstruction combined with convex combination least mean square adaptive filter_Journal of Biomedical Engineering

Authors：

ZHANG Linjia ¹ ,  YU Xiaomin ^1,2 , LIN Jian ³ , CHOU Chengen ³ , WANG Zhengxian ¹

1. Key Laboratory of Biomedical Effect of Physical Field and Instrument, School of Electrical and Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, P. R. China;
2. Chongqing Institute of Microelectronics and Microsystems, Beijing Institute of Techonology, Chongqing 401332, P. R. China;
3. Sichuan Acad Medicine Science & Sichuan Province Peoples Hospital, Chengdu 610072, P. R. China;

Corresponding author：

YU Xiaomin, Email: jonahyxm@126.com

Keywords：

Photoplethysmographic; Blood oxygen saturation; Motion noise; Signal decomposition; Noise reconstruction

DOI：

10.7507/1001-5515.202310053

Video：

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The performance of a pulse oximeter based on photoelectric detection is greatly affected by motion noise (MA) in the photoplethysmographic (PPG) signal. This paper presents an algorithm for detecting motion oxygen saturation, which reconstructs a motion noise reference signal using ensemble of complete adaptive noise and empirical mode decomposition combined with multi-scale permutation entropy, and eliminates MA in the PPG signal using a convex combination least mean square adaptive filters to calculate dynamic oxygen saturation. The test results show that, under simulated walking and jogging conditions, the mean absolute error (MAE) of oxygen saturation estimated by the proposed algorithm and the reference oxygen saturation are 0.05 and 0.07, respectively, with means absolute percentage error (MAPE) of 0.05% and 0.07%, respectively. The overall Pearson correlation coefficient reaches 0.971 2. The proposed scheme effectively reduces motion artifacts in the corrupted PPG signal and is expected to be applied in portable photoelectric pulse oximeters to improve the accuracy of dynamic oxygen saturation measurement.

Citation： ZHANG Linjia, YU Xiaomin, LIN Jian, CHOU Chengen, WANG Zhengxian. Research on dynamic blood oxygen saturation measurement based on motion noise reconstruction combined with convex combination least mean square adaptive filter. Journal of Biomedical Engineering, 2024, 41(4): 818-825. doi: 10.7507/1001-5515.202310053 Copy

1.	Hosanee M, Chan G, Welykholowa K, et al. Cuffless single-site photoplethysmography for blood pressure monitoring. J Clin Med, 2020, 9(3): 723.
2.	Eerikainen L M, Bomoni A G, Dekker L R, et al. Atrial fibrillation monitoring with wrist-worn photoplethysmography-based wearables: state-of-the-art review. Cardiovascular Digital Health Journal, 2020, 1(1): 45-51.
3.	Biswas D, Simões-Capela N, Van Hoof C, et al. Heart rate estimation from wrist-worn photoplethysmography: a review. IEEE Sens J, 2019, 19(16): 6560-6570.
4.	李敏. 基于光电容积脉搏波的抗运动心率及血氧提取算法研究. 北京: 北京理工大学, 2016.
5.	Ansari S, Ward K, Najarian K. Epsilon-tube filtering: reduction of high-amplitude motion artifacts from impedance plethysmography signal. IEEE J Biomed Health Inform, 2015, 19(2): 406-417.
6.	Couceiro R, Carvalho P, Paiva R P, et al. Detection of motion artifact patterns in photoplethysmographic signals based on time and period domain analysis. Physiol Meas, 2014, 35(12): 2369-2388.
7.	Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas, 2007, 28(3): R1-39.
8.	Kim B S, Yoo S K. Motion artifact reduction in photoplethysmography using independent component analysis. IEEE Trans Biomed Eng, 2006, 53(3): 566-568.
9.	Wang S, Gao Z, Li G, et al. Adaptive pulse oximeter with dual-wavelength based on wavelet transforms. Opt Express, 2013, 21(20): 23058-23067.
10.	Raghuram M, Madhav K V, Krishna E H, et al. Dual-tree complex wavelet transform for motion artifact reduction of PPG signals//2012 IEEE International Symposium on Medical Measurements and Applications Proceedings, Budapest: IEEE, 2012: 1-4.
11.	Sun X, Yang P, Li Y, et al. Robust heart beat detection from photoplethysmography interlaced with motion artifacts based on empirical mode decomposition//Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, Hong Kong: IEEE, 2012: 775-778.
12.	Roy B, Gupta R. MoDTRAP: Improved heart rate tracking and preprocessing of motion-corrupted photoplethysmo-graphic data for personalized healthcare. Biomed Signal Proces, 2020, 56: 101676-101689.
13.	Relente A R, Sison L G. Characterization and adaptive filtering of motion artifacts in pulse oximetry using accelerometers//24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society, Houston: IEEE, 2002: 1769-1770.
14.	Chan K W, Zhang Y T. Adaptive reduction of motion artifact from photoplethysmographic recordings using a variable step-size LMS filter//Proceedings of IEEE Sensors, Orlando: IEEE, 2003: 1343-1346.
15.	Ram M R, Madhav K V, Krishna E H, et al. A novel approach for motion artifact reduction in PPG signals based on AS-LMS adaptive filter. IEEE Transactions on Instrumentation and Measurement, 2012, 61(5): 1445-1457.
16.	Gibbs P, Asada H H. Reducing motion artifact in wearable bio-sensors using MEMS accelerometers for active noise cancellation//Proceedings of the 2005 American Control Conference, Portland: IEEE, 2005: 1581-1586.
17.	Ram M R, Madhav K V, Krishna E H, et al. On the performance of AS-LMS based adaptive filter for reduction of motion artifacts from PPG signals//IEEE International Instrumentation and Measurement Technology Conference, Hangzhou: IEEE, 2011: 1536-1539.
18.	Reddy K A, Kumar V J. Motion artifact reduction in photoplethysmographic signals using singular value decomposition//IEEE Instrumentation & Measurement Technology Conference, Warsaw: IEEE, 2007: 1-4.
19.	Barnova K, Martinek R, Jaros R, et al. System for adaptive extraction of non-invasive fetal electrocardiogram. Appl Soft Comput, 2021, 113: 107940-107959.
20.	Ouelaa Z, Younes R, Djebala A, et al. Comparative study between objective and subjective methods for identifying the gravity of single and multiple gear defects in case of noisy signals. Appl Acoust, 2022, 185: 108432-108445.
21.	Zhao L, Li Z, Zhang J, et al. An integrated complete ensemble empirical mode decomposition with adaptive noise to optimize LSTM for significant wave height forecasting. J Mar Sci Eng, 2023, 11(2): 435-457.
22.	钟志贤, 马李奕, 蔡忠侯, 等. 基于VMD_MPE和FCM聚类的变转速工况下转子不平衡故障诊断方法. 振动与冲击, 2022, 41(14): 290-298.
23.	赵建岗,宁静, 宁云志, 等. 基于多尺度排列熵和线性局部切空间排列的机械故障诊断特征提取. 振动与冲击, 2021, 40(13): 136-145.
24.	Arunkumar K R, Bhaskar M. Heart rate estimation from photoplethysmography signal for wearable health monitoring devices. Biomed Signal Proces, 2019, 50: 1-9.
25.	Vazquez A A, Avalos J. Sanchez G, et al. A comparative survey of convex combination of adaptive filters. Journal of the Institution of Electronics and Telecommunication Engineers, 2020, 69(2): 940-950.
26.	黄俊翔. 便携式动态心电、血氧监护仪研制. 桂林: 桂林电子科技大学, 2022.
27.	石振乔. 基于PPG信号的血氧检测算法研究. 海口: 海南大学, 2022.
28.	张林. 运动状态下血氧饱和度检测算法研究. 重庆: 重庆理工大学, 2021.
29.	Sandie A B, Tejiokem M C, Faye C M, et al. Observed versus estimated actual trend of COVID-19 case numbers in cameroon: a data-driven modeling. Infectious Disease Modelling, 2023, 8(1): 228-239.
30.	毛烁. 面向运动过程的血氧饱和度提取算法研究. 西安: 西安电子科技大学, 2018.

1. Hosanee M, Chan G, Welykholowa K, et al. Cuffless single-site photoplethysmography for blood pressure monitoring. J Clin Med, 2020, 9(3): 723.
2. Eerikainen L M, Bomoni A G, Dekker L R, et al. Atrial fibrillation monitoring with wrist-worn photoplethysmography-based wearables: state-of-the-art review. Cardiovascular Digital Health Journal, 2020, 1(1): 45-51.
3. Biswas D, Simões-Capela N, Van Hoof C, et al. Heart rate estimation from wrist-worn photoplethysmography: a review. IEEE Sens J, 2019, 19(16): 6560-6570.
4. 李敏. 基于光电容积脉搏波的抗运动心率及血氧提取算法研究. 北京: 北京理工大学, 2016.
5. Ansari S, Ward K, Najarian K. Epsilon-tube filtering: reduction of high-amplitude motion artifacts from impedance plethysmography signal. IEEE J Biomed Health Inform, 2015, 19(2): 406-417.
6. Couceiro R, Carvalho P, Paiva R P, et al. Detection of motion artifact patterns in photoplethysmographic signals based on time and period domain analysis. Physiol Meas, 2014, 35(12): 2369-2388.
7. Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas, 2007, 28(3): R1-39.
8. Kim B S, Yoo S K. Motion artifact reduction in photoplethysmography using independent component analysis. IEEE Trans Biomed Eng, 2006, 53(3): 566-568.
9. Wang S, Gao Z, Li G, et al. Adaptive pulse oximeter with dual-wavelength based on wavelet transforms. Opt Express, 2013, 21(20): 23058-23067.
10. Raghuram M, Madhav K V, Krishna E H, et al. Dual-tree complex wavelet transform for motion artifact reduction of PPG signals//2012 IEEE International Symposium on Medical Measurements and Applications Proceedings, Budapest: IEEE, 2012: 1-4.
11. Sun X, Yang P, Li Y, et al. Robust heart beat detection from photoplethysmography interlaced with motion artifacts based on empirical mode decomposition//Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, Hong Kong: IEEE, 2012: 775-778.
12. Roy B, Gupta R. MoDTRAP: Improved heart rate tracking and preprocessing of motion-corrupted photoplethysmo-graphic data for personalized healthcare. Biomed Signal Proces, 2020, 56: 101676-101689.
13. Relente A R, Sison L G. Characterization and adaptive filtering of motion artifacts in pulse oximetry using accelerometers//24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society, Houston: IEEE, 2002: 1769-1770.
14. Chan K W, Zhang Y T. Adaptive reduction of motion artifact from photoplethysmographic recordings using a variable step-size LMS filter//Proceedings of IEEE Sensors, Orlando: IEEE, 2003: 1343-1346.
15. Ram M R, Madhav K V, Krishna E H, et al. A novel approach for motion artifact reduction in PPG signals based on AS-LMS adaptive filter. IEEE Transactions on Instrumentation and Measurement, 2012, 61(5): 1445-1457.
16. Gibbs P, Asada H H. Reducing motion artifact in wearable bio-sensors using MEMS accelerometers for active noise cancellation//Proceedings of the 2005 American Control Conference, Portland: IEEE, 2005: 1581-1586.
17. Ram M R, Madhav K V, Krishna E H, et al. On the performance of AS-LMS based adaptive filter for reduction of motion artifacts from PPG signals//IEEE International Instrumentation and Measurement Technology Conference, Hangzhou: IEEE, 2011: 1536-1539.
18. Reddy K A, Kumar V J. Motion artifact reduction in photoplethysmographic signals using singular value decomposition//IEEE Instrumentation & Measurement Technology Conference, Warsaw: IEEE, 2007: 1-4.
19. Barnova K, Martinek R, Jaros R, et al. System for adaptive extraction of non-invasive fetal electrocardiogram. Appl Soft Comput, 2021, 113: 107940-107959.
20. Ouelaa Z, Younes R, Djebala A, et al. Comparative study between objective and subjective methods for identifying the gravity of single and multiple gear defects in case of noisy signals. Appl Acoust, 2022, 185: 108432-108445.
21. Zhao L, Li Z, Zhang J, et al. An integrated complete ensemble empirical mode decomposition with adaptive noise to optimize LSTM for significant wave height forecasting. J Mar Sci Eng, 2023, 11(2): 435-457.
22. 钟志贤, 马李奕, 蔡忠侯, 等. 基于VMD_MPE和FCM聚类的变转速工况下转子不平衡故障诊断方法. 振动与冲击, 2022, 41(14): 290-298.
23. 赵建岗,宁静, 宁云志, 等. 基于多尺度排列熵和线性局部切空间排列的机械故障诊断特征提取. 振动与冲击, 2021, 40(13): 136-145.
24. Arunkumar K R, Bhaskar M. Heart rate estimation from photoplethysmography signal for wearable health monitoring devices. Biomed Signal Proces, 2019, 50: 1-9.
25. Vazquez A A, Avalos J. Sanchez G, et al. A comparative survey of convex combination of adaptive filters. Journal of the Institution of Electronics and Telecommunication Engineers, 2020, 69(2): 940-950.
26. 黄俊翔. 便携式动态心电、血氧监护仪研制. 桂林: 桂林电子科技大学, 2022.
27. 石振乔. 基于PPG信号的血氧检测算法研究. 海口: 海南大学, 2022.
28. 张林. 运动状态下血氧饱和度检测算法研究. 重庆: 重庆理工大学, 2021.
29. Sandie A B, Tejiokem M C, Faye C M, et al. Observed versus estimated actual trend of COVID-19 case numbers in cameroon: a data-driven modeling. Infectious Disease Modelling, 2023, 8(1): 228-239.
30. 毛烁. 面向运动过程的血氧饱和度提取算法研究. 西安: 西安电子科技大学, 2018.

Journal of Biomedical Engineering

Research on dynamic blood oxygen saturation measurement based on motion noise reconstruction combined with convex combination least mean square adaptive filter

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