Novel Approaches for the Removal of Motion Artifact From EEG Recordings
Novel Approaches for the Removal of Motion Artifact From EEG Recordings
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DOI：
10.1109/jsen.2019.2931727
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发表时间：
2019-11-15
影响因子：
4.3
通讯作者：
Pachori, Ram Bilas
中科院分区：
文献类型：
--
作者：
Gajbhiye, Pranjali;Tripathy, Rajesh Kumar;Pachori, Ram Bilas
脑电图（EEG）信号在记录过程中受到各种噪声或伪影的污染。对于神经系统疾病的自动检测，从脑电信号中滤除这些伪影是一项至关重要的任务。本文提出了两种从单通道脑电信号中去除运动伪影的新方法。这些方法基于多分辨率全变分（MTV）和多分辨率加权全变分（MWTV）滤波方案。采用离散小波变换（DWT）的多分辨率分析有助于将脑电信号分离成不同的子带信号。将总变分（TV）和加权变分（WTV）分别应用于近似子带信号。滤波后的近似子带信号根据噪声近似子带信号与TV或WTV滤波器输出之间的差值进行评估。利用多分辨率小波重构得到处理后的脑电信号。用信噪比（Delta SNR）和相关系数降低百分比（eta）的差值来评价处理后的脑电信号的诊断质量。实验结果表明，与现有方法相比，所提出的MTV和MWTV方法具有更好的去噪性能，平均Delta信噪比和平均eta值分别为29.12 dB和68.56%和29.29 dB和67.51%。
The electroencephalogram (EEG) signal is contaminated with various noises or artifacts during recording. For the automated detection of neurological disorders, it is a vital task to filter out these artifacts from the EEG signal. In this paper, we propose two novel approaches for the removal of motion artifact from the single channel EEG signal. These methods are based on the multiresolution total variation (MTV) and multiresolution weighted total variation (MWTV) filtering schemes. The multiresolution analysis using the discrete wavelet transform (DWT) helps to segregate the EEG signal into various subband signals. The total variation (TV) and weighted TV (WTV) are applied to the approximation subband signal. The filtered approximation subband signal is evaluated based on the difference between the noisy approximation subband signal and the output of the TV or WTV filter. The processed EEG signal is obtained using the multiresolution wavelet-based reconstruction. The difference in the signal to noise ratio (Delta SNR) and the percentage of reduction in correlation coefficients (eta) is used for evaluating the diagnostic quality of the processed EEG signal. The experimental results demonstrate that the proposed MTV and MWTV approaches have better denoising performance with (average Delta SNR, and average eta) values of (29.12 dB and 68.56%) and (29.29 dB and 67.51%), respectively, as compared to the existing techniques.