In the complex environment of telemedicine, Electroencephalogram (EEG) signals are easily overwhelmed by noise, which affects the intelligent diagnosis of diseases. Since the time-frequency domain characteristics of some noise in EEG signals are complex and the distribution is unknown, and the spectrum of some noise overlaps with the original EEG signal spectrum, it is difficult to filter those noise by traditional methods. To tackle this problem, and considering the large data characteristics of EEG signals in the context of telemedicine, a wide-deep echo state networks (WDESN) with multiple reservoirs in parallel and stacked configuration is proposed for multivariate time series denoising. Firstly, stacking and paralleling multiple reservoirs, the deep features of signals can be extracted to complete the task of reconstructing signal from the noisy signal. Then, the Uniform Search Particle Swarm Optimization (UPSO) is used to optimize reservoir parameters of WDESN. Finally, the effectiveness of UPSO-WDESN is verified through experiments. Experiment results show that compared with existing models, the proposed UPSO-WDESN model can achieve better noise removal performance, while keeping more nonlinear feature of signals.

在复杂的远程医疗环境中，脑电图（EEG）信号容易被噪声淹没，从而影响疾病的智能诊断。由于EEG信号中某些噪声的时频域特性复杂且分布未知，并且某些噪声的频谱与原始EEG信号频谱重叠，因此很难通过传统方法过滤掉这些噪声。为了解决这个问题，并考虑到远程医疗背景下脑电信号的大数据特征，提出了一种具有多个并行排列和堆叠配置的储层的宽深回波状态网络（WDESN），用于多元时间序列降噪。首先，将多个储层堆叠并平行排列，信号的深层特征可以被提取出来，以完成从噪声信号中重建信号的任务。然后，使用统一搜索粒子群优化算法（UPSO）来优化WDESN的储层参数。最后，通过实验验证了UPSO-WDESN的有效性。实验结果表明，与现有模型相比，提出的UPSO-WDESN模型在保持信号非线性特性的同时，具有较好的噪声去除性能。