基于频率密度的局部离群因子的工频自适应抑制方法

Power Frequency Adaptive Suppression Method Based on Local Outlier Factor of Frequency Density

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。

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,this paper proposes 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,RMS E,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 can be seen proved that the filtering effect in this paper study was better than the conventional adaptive filter.