[1]黄紫娟,涂 娟,代尊翔.基于频率密度的局部离群因子的工频自适应抑制方法[J].郑州大学学报(工学版),2023,44(05):46-52.[doi:10.13705/j.issn.1671-6833.202.05.015]
　HUANG Zijuan,TU Juan,DAI Zunxiang.Power Frequency Adaptive Suppression Method Based on Local Outlier Factor of Frequency Density[J].Journal of Zhengzhou University (Engineering Science),2023,44(05):46-52.[doi:10.13705/j.issn.1671-6833.202.05.015]
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基于频率密度的局部离群因子的工频自适应抑制方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
44
期数:
2023年05期
页码:
46-52
栏目:
出版日期:
2023-08-20

文章信息/Info

Title:
Power Frequency Adaptive Suppression Method Based on Local Outlier Factor of Frequency Density
作者:
黄紫娟1; 2;  涂 娟1; 2;  代尊翔1; 2
1. 福州大学 电气工程与自动化学院 ,福建 福州 350108;2. 福建省医疗器械与医药技术重点实验室,福建 福州 350108
Author(s):
HUANG Zijuan;  TU Juan;  DAI Zunxiang
关键词:
生物电信号;  工频;  自适应;  局部离群因子;  去噪
Keywords:
bioelectrical signal;  power frequency;  self adapting;  local outlier factor;  denoising
分类号:
TP29
DOI:
10.13705/j.issn.1671-6833.202.05.015
文献标志码:
A
摘要:
生物电信号属于强噪声背景下的低频微弱信号,工频干扰的滤除很有必要。 为保证工频偏移时滤波的精 准性和有效性,提出了一种基于频率密度的局部离群因子( FLOF)算法,并结合经验模态分解(EMD)对信号进行自 适应去噪。 首先,将信号进行短时傅里叶变换,将局部离群因子算法拓展到频域,通过 FLOF 找到工频干扰的频率 偏移量和偏移时刻;其次,根据偏移时刻对信号进行分段,使用段内瞬时工频的平均值作为段内实际工频;最后,对 每段信号进行 EMD 分解,生成多个不同时间尺度的局部特征分量,仅对包含工频信号的局部特征分量滤波保留更 多有用信息。 结果表明:此方法频率估计精度较高,在不同信噪比下滤波后信噪比、均方根误差、相似度均得到一 定改善。 以原信噪比-30 dB 为例,相较于最小均方误差滤波和递推最小二乘滤波信噪比提升 16. 266、7. 671 dB,均 方根误差减小 16. 017、4. 388 dB,相似度提升 0. 200、0. 013,可以看出所提方法滤波效果优于常规滤波方法。
Abstract:
Bioelectric signals belong to weak low-frequency signals with strong noise, therefore it is necessary to filter out power frequency interference. In order to ensure the accuracy and effectiveness of the filtering during power frequency offset, local outlier factor based on frequency density, and combines empirical mode decomposition was proposed to carry out adaptive denoising of signals. Firstly, the local outlier factor was used in the frequency domain by the short-time Fourier transform, and the frequency offset and the offset time and frequency were found by FLOF. Secondly, the signal was segmented according to the offset time, and the average instantaneous power frequency within the segment was used as the actual power frequency within the segment. Finally, each signal segment was decomposed by EMD to generate multiple local feature components of different time scales. More useful information could be reserved only for the component filtering containing power frequency signals. The frequency estimation accuracy of this method was high, and the SNR, RMSE, and SIM were improved after filtering in different dB. Taking -30 dB as an example, compared with the least mean square error filtering and recursive least squares filtering, the SNR increases by 16. 266 and 7. 671 dB, the RMSE decreased by 16. 017 and 4. 388 dB, and the SIM increased by 0. 200 and 0. 013. It proved that the filtering effect in this study was better than the conventional adaptive filter.

参考文献/References:

[1] 刘彬, 马少华, 闫广宇. 生物医学信号相似性分析方 法的研究[ J] . 医疗装备, 2017, 30(14) : 50-51. 

LIU B,MA S H,YAN G Y. Research on similarity analysis methods for biomedical signals [ J] . Medical Equipment, 2017, 30(14) :50-51. 
[2] GUEZGOUZ D, CHARIAG D E, RAINGEAUD Y, et al. Modeling of electromagnetic interference and PLC transmission for loads shedding in a microgrid [ J ] . IEEE Transactions on Power Electronics, 2011, 26 ( 3) : 747 -754.
 [3] 张文伟, 底青云, 耿启立, 等. 基于数字递归陷波的 多通道瞬变电磁法周期噪声去除研究[ J] . 物探与化 探, 2020, 44(2) :278-289.
 ZHANG W W, DI Q Y, GENG Q L, et al. The removal of MTEM periodic noise based on digital recursive notching [ J ] . Geophysical and Geochemical Exploration, 2020, 44(2) :278-289. 
[4] 雷文平, 宋圣霖, 郝旺身, 等. 基于 FV-FBE 的滚动轴 承故障诊断研究[ J] . 郑州大学学报(工学版) , 2020, 41(5) : 82-86. 
LEI W P, SONG S L, HAO W S, et al. Fault diagnosis of rolling bearing based on FV-FBE [ J ] . Journal of Zhengzhou University ( Engineering Science) , 2020, 41 (5) : 82-86.
 [5] 刘军. 重力异常频率域分离方法及应用[ J] . 物探与 化探, 1998, 22(6) : 446-451, 445. 
LIU J. The frequency field separation technique of gravity anomalies and its application[ J] . Geophysical and Geochemical Exploration, 1998, 22(6) : 446-451, 445.
 [6] 谷晓彬, 冯国英, 刘建. 自适应滤波算法在微弱振动 测量 中 的 应 用 [ J ] . 红 外 与 激 光 工 程, 2016, 45 (4) : 0417003. 
GU X B, FENG G Y, LIU J. Application of adaptive filtering algorithm in the weak vibration measurement [ J] . Infrared and Laser Engineering, 2016, 45(4) : 0417003. 
[7] 彭良广, 林金朝, 庞宇, 等. 基于自适应滤波的可穿 戴式心电信号检测系统[ J] . 电子技术应用, 2017, 43 (9) : 17-21. 
PENG L G, LIN J Z, PANG Y, et al. Wearable system based on adaptive filter for monitoring ECG signal [ J] . Application of Electronic Technique, 2017, 43(9) : 17- 21.
[8] 任杰, 杨双龙, 王俊翔, 等. 迭代 ICA 与 LEVKOV 联 合的瞬态响应工频干扰抑制方法[ J] . 电子测量与仪 器学报, 2021, 35(12) : 37-44. 
REN J, YANG S L, WANG J X, et al. Anti-power interference method of combining iterative ICA and LEVKOV for transient response[ J] . Journal of Electronic Measurement and Instrumentation, 2021, 35(12) : 37-44.
[9] RILLING G, FLANDRIN P. One or two frequencies? the empirical mode decomposition answers[ J] . IEEE Transactions on Signal Processing, 2008, 56(1) : 85-95. 
[10] 邹清, 汤井田, 唐艳. Hilbert-Huang 变换应用于心电 信号消噪[ J] . 中国医学物理学杂志, 2007, 24( 4) : 309-312. 
ZOU Q, TANG J T, TANG Y. Hilbert-Huang transform for ECG de-noising[ J] . 中国医学物理学杂志, 2007, 24(4) :309-312. 
[11] 邹云峰, 张昕, 宋世渊, 等. 基于局部密度的快速离 群点检 测 算 法 [ J ] . 计 算 机 应 用, 2017, 37 ( 10 ) : 2932-2937. 
ZOU Y F, ZHANG X, SONG S Y, et al. Fast outlier detection algorithm based on local density [ J] . Journal of Computer Applications, 2017, 37(10) :2932-2937.
 [12] 薛明志, 陈商玥, 高强. 基于 k-medoids 聚类算法的低 压台区线损异常识别方法[ J] . 天津理工大学学报, 2021, 37(1) :26-31. 
XUE M Z, CHEN S Y, GAO Q. Recognition method of line loss anomaly in low-voltage station area based on kmedoids clustering algorithm[ J] . Journal of Tianjin University of Technology, 2021, 37(1) :26-31. 
[13] 方海超. 脉冲噪声环境中基于 GSP 的噪声信号突变 52 郑 州 大 学 学 报 (工 学 版) 2023 年 点检测技术研究[D] . 南京: 南京邮电大学,2021. 
FANG H C. Research on detection technology of noise signal abrupt point based on GSP in impulsive noise environment[ D] . Nanjing: Nanjing University of Posts and Telecommunications,2021.
 [14] 徐晓刚, 徐冠雷, 王孝通, 等. 经验模式分解( EMD) 及其应用[ J] . 电子学报, 2009, 37(3) :581-585. 
XU X G, XU G L, WANG X T, et al. Empirical mode decomposition and its application [ J] . Acta Electronica Sinica, 2009, 37(3) :581-585.
 [15] 赵雯雯, 曾兴雯. 一种新的 EMD 去噪方法[ J] . 电子 科技, 2008, 21(5) : 30-32, 36. 
ZHAO W W, ZENG X W. A new signal denoising method based on empirical mode decomposition ( EMD) [ J] . Electronic Science and Technology, 2008, 21( 5) : 30 - 32, 36. 
[16] 杨承金, 聂春燕, 王慧宇, 等. 基于小波改进阈值的 肌电干扰降噪研究与效果评估 [ J] . 电子测量技术, 2021, 44(22) : 80-86. 
YANG C J, NIE C Y, WANG H Y, et al. Research of noise reduction algorithm and effect evaluation about EMG interference based on improved wavelet threshold[ J] . Electronic Measurement Technology, 2021, 44 ( 22 ) : 80-86

更新日期/Last Update: 2023-09-04