Microelectrode recording (MER) signals are world-widely used for validating the planned trajectories in the procedure of deep brain stimulation (DBS) surgery to obtain accurate position of electrodes inside the brain structure. Besides, MER signals are important source for studying extracellular neuronal activity and DBS biomarkers, such as, spike clustering and sorting. However, MER signals are prone to several artifacts derived from electrical equipment in the operating room, electrode movement and patient activities, etc., which reduce the signal-to-noise ratio of the MER signals. Therefore, in this paper, we propose a novel deep learning architecture based on long short-term memory (LSTM) network for automatic artifact detection in MER signals. Frequency and time-domain features were extracted from the raw MER signals and fed to the deep LSTM network. A manually annotated MER database obtained from 17 Parkinson's disease (PD) patients were used to validate the proposed architecture. The proposed architecture achieved promising results of 97.49% accuracy, 98.21% sensitivity and 96.87% specificity on an unseen test set. To our best knowledge, this is the first study to use LSTM network for artifacts detection in MER signals. The MER data will be available at http://homepage.hit.edu.cn/wpgao.

微电极记录（MER）信号已在全球范围内用于验证深部脑刺激（DBS）手术过程中的计划轨迹，以获取电极在脑结构内部的准确位置。此外，MER信号是研究细胞外神经元活动和DBS生物标志物（如峰簇和分类）的重要来源。但是，MER信号容易从手术室的电气设备，电极移动和患者活动等中衍生出一些假象，从而降低了MER信号的信噪比。因此，在本文中，我们提出了一种基于长短期记忆（LSTM）网络的新型深度学习架构，用于MER信号中的自动伪像检测。从原始MER信号中提取出频域和时域特征，并馈入深度LSTM网络。从17名帕金森氏病（PD）患者获得的手动注释MER数据库用于验证所提出的体系结构。提出的架构在看不见的测试集上实现了97.49％的准确性，98.21％的灵敏度和96.87％的特异性的有希望的结果。据我们所知，这是第一项使用LSTM网络进行MER信号伪像检测的研究。MER数据可从http://homepage.hit.edu.cn/wpgao获得。据我们所知，这是第一项使用LSTM网络进行MER信号伪像检测的研究。MER数据可从http://homepage.hit.edu.cn/wpgao获得。据我们所知，这是第一项使用LSTM网络进行MER信号伪像检测的研究。MER数据可从http://homepage.hit.edu.cn/wpgao获得。