基于联合稀疏谱重构的PPG信号降噪算法

doi:10.3969/j.issn.0372-2112.2017.07.014

电子学报 ›› 2017, Vol. 45 ›› Issue (7): 1646-1652.DOI: 10.3969/j.issn.0372-2112.2017.07.014

基于联合稀疏谱重构的PPG信号降噪算法

熊继平¹, 蒋定德², 蔡丽桑¹, 汤清华¹, 何小卫¹

1. 浙江师范大学数理与信息工程学院, 浙江金华 321004;
2. 东北大学计算机科学与工程学院, 辽宁沈阳 110819

收稿日期:2015-11-12 修回日期:2016-04-11 出版日期:2017-07-25
- 作者简介:
- 熊继平,男,浙江师范大学数理与信息工程学院副教授,研究生导师,研究方向为压缩感知算法及应用.E-mail:xjping@zjnu.cn;蒋定德,男,博士,东北大学计算机科学与工程学院教授,博士生导师,主要研究方向为网络测量、网络安全、软件定义网络和认知网络等.E-mail:jiangdd@mail.neu.edu.cn;蔡丽桑,女,浙江师范大学数理与信息工程学院硕士研究生,研究方向稀疏信息处理和压缩感知.E-mail:574585714@qq.com;何小卫,男,浙江师范大学数理与信息工程学院副教授,民盟浙师大委员会在职总支第一支部主委.研究方向模糊系统与粗糙集、计算机应用.E-mail:jhhxw@zjnu.cn
- 基金资助:
- 浙江省自然科学基金 (No.LY14F010008）; 国家自然科学基金 (No.61572023）

An Algorithm of Motion Artifact Reduction in PPG Signals Based on Joint Sparse Spectrum Reconstruction

XIONG Ji-ping¹, JIANG Ding-de², CAI Li-sang¹, TANG Qing-hua¹, HE Xiao-wei¹

1. College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua, Zhejiang 321004, China;
2. College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819

Received:2015-11-12 Revised:2016-04-11 Online:2017-07-25 Published:2017-07-25
- Supported by:
- National Natural Science Foundation of Zhejiang Province, China (No.LY14F010008); National Natural Science Foundation of China (No.61572023)

摘要/Abstract

摘要：

针对光电容积脉搏波（Photoplethysmography，PPG）传感器数据采集降噪问题，本文提出一种基于联合稀疏重构的PPG信号运动噪声降噪算法.该算法通过构建同时间段内PPG信号和加速度信号的频谱矩阵，提取频谱矩阵稀疏特征和该矩阵行稀疏特征，利用压缩感知方法，将PPG信号运动噪声去除过程建模为联合稀疏信号重构过程，并将该过程进一步建模为最优化模型，通过迭代寻优来获得该模型的最优解，结合谱减法，从而有效去除PPG信号中的运动噪声，降低噪声对PPG信号的影响.仿真分析表明，本文提出的算法能有效去除PPG信号中的运动噪声，获得较好的降噪效果.

关键词: 光电容积脉搏波, 心率测量, 稀疏信号重构, 压缩感知

Abstract:

This paper proposes a joint sparse spectrum reconstruction-based motion artifact reduction algorithm for Photoplethysmo-graphy (PPG) signals to overcome the artifact removing problem in the PPG sensor data collection.Firstly,our algorithm constructs a spectral matrix,using PPG signals and acceleration signals during the same time period.The sparse characteristics of the spectral matrix and its rows are extracted.Secondly,we use the compressive sensing to model the motion artifact removing process in PPG signals as a joint sparse signal reconstruction process.Then this process is further modeled as an optimal model.We exploit the iterative method to obtain the optimal solution to the model.Finally,we combine the spectrum subtraction to remove the motion artifact in PPG signals.In the result,we can effectively decrease the impact of the motion artifact on PPG signals.Simulation results demonstrate that the algorithm proposed in this paper can effectively remove the motion artifact in PPG signals and attain the better noise reduction performance.

Key words: photoplethysmography (PPG), heart rate monitoring, sparse signal reconstruction, compressive sensing

中图分类号:

TP393

熊继平, 蒋定德, 蔡丽桑, 等. 基于联合稀疏谱重构的PPG信号降噪算法[J]. 电子学报, 2017, 45(7): 1646-1652.

XIONG Ji-ping, JIANG Ding-de, CAI Li-sang, et al. An Algorithm of Motion Artifact Reduction in PPG Signals Based on Joint Sparse Spectrum Reconstruction[J]. Acta Electronica Sinica, 2017, 45(7): 1646-1652.

参考文献

[1] Maeda Y,Sekine M,Tamura T.Relationship between measurement site and motion artifacts in wearable reflected photoplethysmography.[J].Journal of Medical Systems,2011,35(5):969-976.
[2] Kim B S,Yoo S K.Motion artifact reduction in photoplethysmography using independent component analysis[J].Biomedical Engineering IEEE Transactions on,2006,53(3):566-568.
[3] Raghuram M,Madhav K V,Krishna E H,et al.Evaluation of wavelets for reduction of motion artifacts in photoplethysmographic signals[A].IEEE International Conference on Information Sciences Signal Processing and their Applications (ISSPA)[C].2010.460-463.
[4] Fukushima H,Kawanaka H,Bhuiyan M S,et al.Estimating heart rate using wrist-type photoplethysmography and acceleration sensor while running.[A].IEEE Engineering in Medicine and Biology Society.Annual Conference[C].2012.2901-2904.
[5] Sun X,Yang P,Li Y,et al.Robust heart beat detection from photoplethysmography interlaced with motion artifacts based on empirical mode decomposition[A].IEEE International Conference on Biomedical and Health Informatics (BHI)[C].2012.775-778.
[6] 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[J].Instrumentation & Measurement IEEE Transactions on,2012,61(5):1445-1457.
[7] Lee B,Han J,Baek H J,et al.Improved elimination of motion artifacts from a photoplethysmographic signal using a Kalman smoother with simultaneous accelerometry[J].Physiological Measurement,2010,31(12):1585-603.
[8] Shafqat K,Langford R M,Pal S K,et al.Estimation of venous oxygenation saturation using the finger photoplethysmograph (PPG) waveform.[A].IEEE Engineering in Medicine and Biology Society[C].2012.2905-2908.
[9] Zhang Z,Pi Z,Liu B.TROIKA:a general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise.[J].IEEE Transactions on Biomedical Engineering,2014,62(2):522-531.
[10] Zhang Z.Photoplethysmography-based heart rate monitoring in physical activities via joint sparse spectrum reconstruction[J].IEEE Transactions on Biomedical Engineering,2015,62:1902-1910.
[11] 沈燕飞,朱珍民,张勇东,李锦涛.基于秩极小化的压缩感知图像恢复算法[J].电子学报,2016,44(3):572-579. Shen Yan-fei,Zhu Zhen-min,Zhang Yong-dong,Li Jin-tao.Compressed sensing image reconstruction algorithm based on rank minimization[J].Acta Electronica Sinica,2016,44(3):572-579.(in Chinese)
[12] 王斌,胡辽林,曹京京,薛瑞洋,刘光飞.基于小波域稀疏最优的图像修复方法[J].电子学报,2016,44(3):600-606. Wang Bin,HU Liao-lin,Cao Jing-jing,Xue Rui-yang,Liu Guang-fei.Image restoration based on sparse-optimal strategy in wavelet domain[J].Acta Electronica Sinica,2016,44(3):600-606.(in Chinese)
[13] Cotter S F,Rao B D,Engan K,et al.Sparse solutions to linear inverse problems with multiple measurement vectors[J].IEEE Transactions on Signal Processing,2005,53(7):2477-2488.
[14] Peng Y,Ganesh A,Wright J,et al.RASL:Robust alignment by sparse and low-rank decomposition for linearly correlated images[A].IEEE Conference on Computer Vision and Pattern Recognition (CVPR)[C].2010:763-770.
[15] Zhang T,Liu S,Ahuja N,et al.Robust visual tracking via consistent low-rank sparse learning[J].International Journal of Computer Vision,2014,111(2):171-190.
[16] Liu J,Ji S,Ye J.Multi-task feature learning via efficient l 2,1-norm minimization[A].In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence[C].2009:339-348.
[17] Bland J M,Altman D G.Statistical methods for assessing agreement between two methods of clinical measurement[J].The lancet,1986,327(8476):307-310.

基于联合稀疏谱重构的PPG信号降噪算法

An Algorithm of Motion Artifact Reduction in PPG Signals Based on Joint Sparse Spectrum Reconstruction

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	陈胜垚, 席峰, 张素玲, 刘中. 正交压缩采样系统时域分析[J]. 电子学报, 2023, 51(3): 573-584.
[2]	吕明久, 马建朝, 韦旭, 陈文峰, 杨军, 马晓岩. 基于矩阵化原子范数的高频雷达距离-多普勒二维估计方法[J]. 电子学报, 2022, 50(5): 1150-1158.
[3]	苏林林, 陈亮, 陈菲菲, 周鑫, 焦振航, 刘钊良. 面向B5G/6G的GFDM信号高精度测距与定位研究[J]. 电子学报, 2022, 50(4): 849-859.
[4]	魏志超, 杨春玲. 时域注意力特征对齐的视频压缩感知重构网络[J]. 电子学报, 2022, 50(11): 2584-2592.
[5]	陈文俊, 杨春玲. 图像压缩感知的特征域优化及自注意力增强神经网络重构算法[J]. 电子学报, 2022, 50(11): 2629-2637.
[6]	左婷, 王法松, 张建康, 李睿. 室内可见光通信系统中基于压缩感知的空移键控信号检测方法[J]. 电子学报, 2022, 50(1): 36-44.
[7]	冯西安, 寇思玮, 谭伟杰, 毕杨. 水声信号处理中的稀疏表示理论及应用[J]. 电子学报, 2021, 49(9): 1840-1851.
[8]	裴翰奇, 杨春玲, 魏志超, 曹燕. 基于SPL迭代思想的图像压缩感知重构神经网络[J]. 电子学报, 2021, 49(6): 1195-1203.
[9]	禤韵怡, 杨春玲. 基于帧间组稀疏的两阶段递归增强视频压缩感知重构网络[J]. 电子学报, 2021, 49(3): 435-442.
[10]	李峰, 詹邦成, 辛蕾, 刘洋, 刘志佳, 肖化超. 基于新型联合感知矩阵的压缩学习目标识别技术[J]. 电子学报, 2021, 49(11): 2108-2116.
[11]	郑恩明, 陈新华, 周权斌, 李嶷, 杨鹤, 孟浩. 一种复域压缩感知目标方位估计方法[J]. 电子学报, 2021, 49(11): 2117-2123.
[12]	孙泽宇, 李传锋, 阎奔. 传感网中基于压缩感知的丢包匹配数据收集算法[J]. 电子学报, 2020, 48(4): 723-733.
[13]	陈兵兵, 曹欣远, 陈明生, 齐琦, 况晓静, 吴先良, 陈辉. 基于欠定方程的频谱峰值搜索算法及其在连续毫米波雷达系统中的应用[J]. 电子学报, 2020, 48(2): 369-374.
[14]	郑学炜, 杨春玲, 禤韵怡. CVS中基于视频运动特征的多假设-双稀疏重构算法[J]. 电子学报, 2020, 48(2): 249-257.
[15]	马彬, 王宏明, 谢显中. 基于二项分布改进的宽带压缩频谱检测方案[J]. 电子学报, 2020, 48(2): 243-248.