Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Genoa, Italy.
Department of Electrical and Electronic Engineering (DIEE), University of Cagliari, Cagliari, Italy.
J Neural Eng. 2020 Dec 16;17(6). doi: 10.1088/1741-2552/abc741.
Among the different approaches for denoising neural signals, wavelet-based methods are widely used due to their ability to reduce in-band noise. All wavelet denoising algorithms have a common structure, but their effectiveness strongly depends on several implementation choices, including the mother wavelet, the decomposition level, the threshold definition, and the way it is applied (i.e. the thresholding). In this work, we investigated these factors to quantitatively assess their effects on neural signals in terms of noise reduction and morphology preservation, which are important when spike sorting is required downstream.Based on the spectral characteristics of the neural signal, according to the sampling rate of the signals, we considered two possible decomposition levels and identified the best-performing mother wavelet. Then, we compared different threshold estimation and thresholding methods and, for the best ones, we also evaluated their effect on clearing the approximation coefficients. The assessments were performed on synthetic signals that had been corrupted by different types of noise and on a murine peripheral nervous system dataset, both of which were sampled at about 16 kHz. The results were statistically analysed in terms of their Pearson's correlation coefficients, root-mean-square errors, and signal-to-noise ratios.As expected, the wavelet implementation choices greatly influenced the processing performance. Overall, the Haar wavelet with a five-level decomposition, hard thresholding method, and the threshold proposed by Han(2007) achieved the best outcomes. Based on the adopted performance metrics, wavelet denoising with these parametrizations outperformed conventional 300-3000 Hz linear bandpass filtering.These results can be used to guide the reasoned and accurate selection of wavelet denoising implementation choices in the context of neural signal processing, particularly when spike-morphology preservation is required.
在神经信号去噪的各种方法中，基于小波的方法因其能够降低带内噪声而被广泛应用。所有的小波去噪算法都具有共同的结构，但它们的有效性强烈依赖于几个实现选择，包括母小波、分解层次、阈值定义以及应用方式（即阈值处理）。在这项工作中，我们研究了这些因素，以便根据噪声减少和形态保持这两个重要指标，对它们在神经信号中的影响进行定量评估，而这在需要后续进行尖峰分类时是很重要的。
根据神经信号的频谱特征，并根据信号的采样率，我们考虑了两种可能的分解层次，并确定了性能最佳的母小波。然后，我们比较了不同的阈值估计和阈值处理方法，并对性能最佳的方法，评估了它们对清除逼近系数的影响。评估是在已被不同类型噪声污染的合成信号和大约 16 kHz 采样的鼠外周神经系统数据集上进行的。结果根据其皮尔逊相关系数、均方根误差和信噪比进行了统计学分析。
正如预期的那样，小波实现选择极大地影响了处理性能。总体而言，具有五级分解的 Haar 小波、硬阈值处理方法以及 Han（2007）提出的阈值实现了最佳结果。基于所采用的性能指标，这些参数化的小波去噪在神经信号处理中优于传统的 300-3000 Hz 线性带通滤波。
这些结果可用于指导在神经信号处理中合理准确地选择小波去噪实现选择，特别是在需要保留尖峰形态时。
